generalization schwartz social values scale 1...

Generalization Schwartz Social Values Scale 1

RUNNING HEAD: Generalization Schwartz Social Values Scale

Generalization and Limitation of the Schwartz Social Values Scale

Jesse M. Dostal

Cleveland States University

2004


ABSTRACT

This study sought to examine the Schwartz Social Values Scale (SVS) through Classic

Multidimensional Scaling (CMDS) and Weighted Multidimensional Scaling (WMDS)

models which were to be analyzed via Multiple Discriminant Functions (MDF) and

binary logistic regression. A normal adult sample from the United States, Poland, and

Romania, composed of 98, 201, and 198 respondents respectively, was obtained. Due to

issues of potential non-representative sampling in the three countries of interest, multiple

linear regression was used to control for the effect of demographics on the individual

SVS item scores. ALSCAL could not produce a satisfactory WMDS model, but did

produce CMDS models for each country individually that were satisfactory. These

models tended to offer partial support of the Schwartz theory. The implications of the

results are that the SVS’s structure of item interrelationships differ by culture, but the

grouping of values into 10 domains and two semi-bidirectional dimensions was

supported.


EXECUTIVE SUMMARY

That peoples of separate cultures differ may be seen as almost self evident in

nature. However, the quantification of these differences requires that the researcher find

an objective measure theoretically tied to culture. Since values are an integral part of

culture, this study focuses upon the quantification of cultural values. In so doing it

provides reviews of three survey based methods of quantifying values; those of Rokeach,

Hofstede, and Schwartz. Schwartz’s (1992) questionnaire and model, in the form of the

Schwartz Values Scale (SVS), were selected as the instrument of choice due to it high

level of validation and refined inclusion of previous researchers’ work.

Schwartz (1992) specifies that the 56 value statements of the SVS fall into ten

primary motivation categories. Further, he states that these ten primary motivators are

arranged into two bidirectional dimensions; Self-Transcendence versus Self-

Enhancement, and Conservation versus Openness to Change.

However, the SVS is typically analyzed via oblique factor analysis or more

commonly Smallest Space Analysis (an early form of Multidimensional Scaling (MDS)

commonly referred to as, SSA). Both of these analysis techniques rely on principal

components; as such they have several limitations. That is, these techniques can be very

resource intense due to the large number of respondents required to achieve reliability.

One of this study’s objectives is to examine the use of alternate MDS models that may

require fewer respondents and provide an alternative examination of the SVS structure of

inter-item association. As such, the hypotheses were tested that MDS solutions would be

congruent between cultures and would verify the prior description of the Schwartz model.


Respondents for this study were composed of a normal adult sample of 98

Americans, 201 Poles, and 198 Romanians. This study’s sample was obtained through

convenience sampling methods. Questionnaires were put through a rigorous double back

translation process. In this case, double back translation means that each country’s

questionnaire was first composed in English and translated to its respective language.

These translations were then back translated into English and any item showing heavy

divergence from the original composition was translated again via another native speaker

of the language. These new translations repeated the process of back translation to

English through another back translator. This process was repeated until the

questionnaires provided clear, concise, versions of the original English copy.

The analysis of this study’s data was composed of four phases. Phase One

involved the alignment of demographic questions for later statistical control. That is, the

respondents having been obtained through convenience methods were potentially non-

representative of each country’s population as a whole. As such, Phase Two of this study

involved the production of linear regression models for which the independent variables

were the demographics and the dependent variable for each item of the SVS. Phase

Three of the analysis involved the production of correlation matrices composed of SVS

items for which the data were standardized residuals from the previously mentioned

multiple regression models. These correlation matrices were then used to generate a

Weighted Multidimensional Scaling (WMDS) model using the data from all three

countries and Classic Multidimensional Scaling (CMDS) models for each country

individually. The final phase of this analysis, Phase Four, involved the use of Multiple

Discriminant Functions (MDF) to test for the presence of Schwartz’s (1992) ten primary


motivation types and binary logistic regression to test for the presence of the semi-

bidirectional dimensions. Finally, the distances generated for each country via CMDS

and the original indices of association for each individual country were tested for

congruence.

The Second Phase of this analysis, the demographic control of SVS items via

linear regression, indicated that the demographics rarely achieved significance. Results

of the Third Phase of this analysis indicate that the WMDS model was a poor fit to the

data. However, the CMDS models generated for each country separately offered

satisfactory solutions. The use of MDF to classify the value statement of the SVS via

Schwartz’s (1992) model received mixed support. Indices of association between the

correlation matrices and CMDS distances by country were low (under .25 in all cases).

The implication of ALSCAL failing to produce a satisfactory WMDS model for

the SVS values of all three countries and the illustration of poor congruence between the

indices of association between the countries’ CMDS distances and association

coefficients suggests that each country of interest in this study maintains unique value

structures. This suggests that the primary motivation and semi-bidirectional dimension

systems is better conceptualized as a taxonomy or classification of values. When the

various CMDS models were tested via MDF and binary logistic regression, the Schwartz

(1992) model of primary motivations appears relatively intact in the three countries.

Notably, this taxonomy was best supported within the Unites States CMDS space, with

Poland coming in second, and Romania third. Implications of these results are discussed.


INTRODUCTION

In an age of increasing globalization many of the social sciences have been faced

with the impact of differing cultural backgrounds upon the particular issues being

investigated. Fortunately, methodologies and theoretical paradigms are available to

quantify and explain these differences. This study seeks to address the measurement of

culture through a commonly used method, questionnaires, and through a commonly

measured component of cultures, values. The purpose of this study is to measure the

interrelationships of values in a cross-cultural context. Specifically, the present paper

will test the use of one of these measures, the Schwartz Social Values Scale (SVS), under

a different analysis model than was originally employed.

Justification for this study’s choice of methodology and system of measurement

for culture will be considered on in the course of this discussion. First to be discussed in

this study will be the method of data collection or instrumentation and the underlying

theory of culture and its relationship to values. Specifically, the first discussion will

address the mode of data collection, questionnaires, and why they are used. The second

discussion will give an overview of available theoretic models describing the data

collected.

Data Collection and Theory

If one is to measure culture then an operational definition is needed. A common

definition would be “the values, ethics, rituals, traditions, material objects, and services

produced or valued by the members of a society (Solomon, 1999).” This definition

suggests that one could measure a variety of subjects. For example, one could count the

number of, material objects, such as cars possessed by members of a culture, amount of a


given behavior, as in the amount of time driving cars, or rate the importance of a given

object or behavior, as in rating the importance of automobiles or the importance of

driving, or one does when driving a car could be recorded. The methodologies that could

be used to collect these data, raw counting, ethnography, or content analysis, are

unimportant in this discussion as there is fundamental flaw associated with information

collected: lack of generalizability.

The concept of generalizability could be defined as whether the outside research

is applicable to a new user’s situation (Guion, 1998). In and of themselves these

behavioral data are situation specific. How does one know that behavior as measured in

one situation takes on the same meaning in another with regard to overall cultural

influence? These behaviors may be bound to the situation. Furthermore, problems are

incurred researching these behaviors. If respondent recall is used as a measure reliability

tends to be a problem (Hurt, Joseph, & Cook, 1977; Goldsmith & Hofacker, 1991).

Further, from an intercultural standpoint values take on meaning only if they maintain the

same definition by way of content. That is, a value is only meaningful if it maintains the

same definition across several cultures (Bardi & Schwartz, 2001; Sverko, 1995).

However, how can the researcher be assured that the definitions of each value within the

context of a behavioral measure will maintain the same meaning across cultures? This

problem of generalizability of measures is very similar to the one faced by innovativeness

and opinion leadership research at a time when a similar debate raged in consumer

psychology. The solution in this case is the same: a higher level of abstraction is needed.

This abstraction might be obtained by using questionnaires in which respondents

rate the overall importance of a given value. Remember that values are integral to the


definition of culture and therefore serve as a proxy for it. From this viewpoint, values are

presumed to encapsulate the aspirations of individuals and societies: they pertain to what

is desirable, to deeply engrained standards that determine future directions and justify

past actions (Braithwaite & Scott, 1991; Bardi & Schwartz, 2001). The end result is that

quantified values can be seen as a distillation of culture. Further, differences in the rated

importance of values between two cultures allow researchers to identify the unique

influences of social structure and culture.

What Are Values?

There are two competing views with regard to values. The first is that a value is

an attribute of the person “doing the valuing.” The second is that a value is an attribute

of an “object receiving the valuing.” To clarify, the first statement assumes that a value

is based in the world of the individual or consensus of individuals and exists as a sort of

conceptual definition. The second statement assumes that a value is an inherent feature

of an object or class of objects. This study, like most other studies of social values, takes

the former as opposed to the latter assumption (Braithwaite & Scott, 1991). From this

viewpoint, values are defined as desirable transituational goals that vary in importance by

individual (Braithwaite & Scott, 1991; Schwartz & Bardi, 2001). Further, values serve as

guiding principles in people’s lives (Schwartz & Bardi, 2001). Under this methodology

the assumption is made that by aggregating the values espoused by individuals, one may

understand values within a given society. Note that this is a common assumption made

by most cross-cultural researchers when studying values with relation to cultures

(Braithwaite & Scott, 1991).


The Quantification of Values

Now that values have been defined, a discussion of how they are quantified is

presented. This discussion will present the theoretical justification for the choice of

instruments to measure values and the selection of cultures to be studied. In the past

three measures of values, those of Rokeach, Hofstede, and Schwartz, have been used in

the study of values within Poland and the United States. Each of these measures compare

the importance of values for these cultures. Note that at the date of this study no known

research has been performed on the quantification of values for the third country of

interest, Romania.

The Rokeach Approach.

Rokeach is seen as dominating values research due to his contributions to

clarifying and integrating the initial concepts of this area (Braithwaite & Scott, 1991;

Kelly, Silverman, & Cochrane, 1972). The Rokeach Value Survey relies on the rank

ordering of 18 terminal values and 18 instrumental values (Solomon 2002; Braithwaite &

Scott, 1991). A terminal value relates to a desirable end state of existence and its

worthiness of attainment. An instrumental value relates to an enduring belief regarding a

certain mode of conduct or manner of behaving. Both are viewed as necessary in the

assessment of a single value. The theoretic rationale supporting the use of two types of

statements revolves around the idea that while desirable end states are important so are

the means of getting to these end states (Waters 1999; Schwartz, 1992).

The values of Rokeach’s Values Survey are found in column one of Table 1. All

values presented therein are rated on a mono-dimensional four-point scale. An

incomplete sentence stem was, “It is now or will be important for me to (value


iteration).” Note that value iterations were used for instrumental values in a different

form; examples are presented in the second column of Table 1. Rokeach’s Values

Survey is composed of fifty-four items (Ferreir-Marques & Miranda, 1995). Finally, a

four-point scale is used to measure the importance of each value iteration. These four

points are anchored as follows:

1. Of little or no importance

2. Of some importance

3. Important

4. Very Important

There are two criticisms of Rokeach’s Values Survey. The first concern is that

this particular instrument is not considered to explain all of the values present in a

culture. Second, Rokeach’s Values Survey is considered to be biased towards Western

values (Spini, 2003; Braithwaite & Scott, 1991). That is, this instrument views values

from a Western viewpoint alone, with only limited influences from Eastern viewpoints.

The Hofstede Approach.

Another values researcher that has looked at both Poland and the United States is

Hofstede. The Hofstede Values Survey relies on five independent value dimensions

(International Business Center, 2003; Middleton & Jones, 2000; Randall, Hwo, &

Pawelk, 1993). Unlike the values measures of Schwartz and Rokeach, Hofstede’s values

survey relies on bi-directional dimensions1. These five value dimensions are Power

Distance, Uncertainty Avoidance, Individualism versus Collectivism, Masculinity versus

1 To clarify the concept of bi-directionality, Hofstede’s measure assumes that, Individualism is the opposite of Collectivism. Further, this measure presupposes that, as one values Individualism less, one must value Collectivism more.


Femininity, and Long versus Short Term Time Orientation. For the purposes of later

discussion each of these dimensions is defined in the following paragraphs

Power Distance is the extent to which a society accepts unequal distribution of

power in institutions and organizations. Although inequality may exist within any culture

the degree to which it is accepted varies considerably between cultures. Power Distance

measures how subordinates respond to power and authority. In high-Power Distance

countries, subordinates tend to be afraid of their bosses, and bosses tend to be

paternalistic and autocratic. In low-power distance countries, subordinates are more

likely to challenge bosses and bosses tend to use a consultative style.

The index of Uncertainty Avoidance focuses on the level of tolerance for

uncertainty and ambiguity within a society for unstructured situations. When Uncertainty

Avoidance is strong, a culture tends to perceive unknown situations as threatening and

people tend to avoid them. This creates a rule-oriented society that institutes laws, rules,

regulations, and controls in order to reduce the amount of ambiguity. A low Uncertainty

Avoidance rating indicates the country has less concern about ambiguity and uncertainty

and has more tolerance for a variety of opinions. Societies that are less rule-oriented,

more readily accept change, take more, and greater risks are archetypal of a low rating on

the Uncertainty Avoidance index.

The next index in the Hofstede Values Survey is Individualism versus

Collectivism. In Individualistic cultures, people are expected to look out for themselves.

Everyone contributes to a common goal, but with little mutual pressure. The typical

attributes endorsed in cultures associated with a high score on this index are personal

time, freedom, and challenge. In Collectivistic countries, people are bound together


through strong personal and protective ties based on loyalty to the group during one’s

lifetime and often beyond. Further, in Collectivistic cultures the typical attributes of

importance are training, physical condition, and the use of skills.

Masculinity versus Femininity is the fourth index in Hofstede’s model. Where

the masculine index is high people tend to value having a high opportunity for earnings,

getting the recognition they deserve when doing a good job, having an opportunity for

advancement to higher-level jobs, and having challenging work to do in order to derive a

sense of accomplishment. In cultures where feminine values are more important, people

tend to value good working relationships with their supervisors. Feminine values under

this framework involve working with people, cooperating well with one another, living in

an area desirable to themselves and to their families, and having the security of working

for their company as long as they want.

The final index in Hofstede’s model is Long-Term versus Short-Term Time

Orientation. A Long-Term Time Orientation is characterized by persistence and

perseverance, a respect for hierarchical status based relationships (how status is assigned

is not addressed in the available literature), thrift, and a sense of shame. A short-term

time orientation is marked by a sense of security and stability, protection of one’s

reputation, a respect for tradition, and a reciprocation of greetings; favors and gifts. As a

side note, this final index was added later on to the Hofstede framework.

Like Rokeach’s Value’s Survey, limitations to Hofstede’s system also exist.

Hofstede’s values system was produced only within modernized Western nations. As

such, it remains unclear how Eastern cultures would fare under this system. The next

criticism revolves around whether each value maintains the same definition between


individual cultures and if the overall structure of the values holds up between cultures

(Schwartz, 1992). To elucidate, it is unknown whether each culture defines each value

the same way, or if these values maintain the same relationship between the individual

dimensions. A further criticism of Hofstede’s measure is that when constructing his

values survey Hofstede relied on a single multinational corporation. The validation of a

values measure on one set of people cross-nationally limits the generalizability through

selection bias. That is, no evidence has been presented explaining whether these values

as developed, measured, and originally normed by Hofstede reflect those of only that

multi-national corporation, or the cultures in which they were measured as a whole.

Additional criticisms revolve around the measure’s age, the data used to generate

Hofstede’s schema are from 1967 through 1973. Thus, there is the danger that these

values reflect only the thinking of that period alone (Schwartz, 1994). This system of

values also makes a rather large assumption that bi-directional scales are appropriate with

regard to individualism versus collectivism and masculinity versus femininity. No

validation of the bi-directionality assumption of these scales can be found within the

literature at hand. As such, this instrument is only as good as this assumption (Schwartz,

1992). A final factor that is not addressed is the independence of each dimension with

regard to the other dimensions. Are these dimensions interrelated? If these dimensions

are interrelated then to what degree are they interrelated? Further, what would be the

impact of these intercorrelations (Schwartz, 1992)?

Schwartz.

For Schwartz and many cross-cultural researchers, cultural values are measured

on two levels: individual and cultural. The individual level variables in values research


measure the psychological dynamics from the framework of the lowest unit of culture,

one person. Cultural level variables measure the solutions societies produce for

regulating human activities (Schwartz, 1994). Note that this study will be measuring

values at the individual level and not at the cultural level. While the interrelationships of

the SVS values for Poland and the United States at the individual level are present in

Schwartz (1992) in the form of an SSA perceptual space map, the need to justify the

countries used in this study (addressed later) via aggregate value profiles are only

available at the cultural level. As such, this distinction is presented here not only to

clarify what level of measurement is being used, but also to inform the reader for a later

portion of this introduction.

The results of the cultural and individual levels of measurement on the SVS share

a multitude of similarities. The same questionnaires are used to measure values at both

levels. Further, the same analysis is used to observe the grouping of values for each

level, smallest space multiple dimensional scaling based on Pearson correlation

coefficients, also known as Smallest Space Analysis. This allows a graphical

presentation of the intercorrelations of each scale as well as raw coefficients. Further, it

solves the problem of assumed bi-directionality found in The Hofstede Values Scale by

allowing all of the values to intercorrelate freely.

Unlike the measures of Rokeach and Hofstede, the SVS was validated with data

from two groups, schoolteachers and college undergraduates. Further, the SVS’s

validation took place in a multitude of cultures. While convenience in obtaining a

sample, on the part of the primary researchers, was an issue in the selection of the two

validation groups other factors also played a role in this decision. First, teachers are one


of the primary groups to communicate values and students are typically seen as the target

of this communication. Second, both populations are easily accessible to researchers. As

such, secondary researchers would be able to easily be able to find respondents when

comparing results (Schwartz, 1994; 1992).

The SVS is composed of 56 single values that represent 10 overarching primary

motivation types. Their descriptions are present in Table 2 of this study. Each item is

measured on a scale composed of nine points varying from negative one to seven. Seven

represents “supreme importance”, zero represents “not important”, finally, negative one

represents “opposed to my values.” Note that two through six remain unlabelled through

this instrument. The SVS is provided in its English form is provided in Appendix A.

At a higher level of abstraction, the primary motivation types may be distilled into

two basic value dimensions (Schwartz, 1992). These dimensions are Self-Transcendence

versus Self-Enhancement and Openness to Change versus Conservation. The

relationship of these value dimensions to the individual level motivation types is

illustrated along with the position of each primary motivation type showing their inter-

associations in Figure 1. Self-Transcendence is akin to Hofstede’s Collectivism, while

Self-Enhancement is similar to Collectivism. Openness to Change is just that, it relates

how accepting an individual is to new ideas and viewpoints as well as how likely he or

she is to change their viewpoint. Conservation is the degree of how steadfast a person is

with regard to her beliefs.

Note that these semi-bipolar value dimensions are not fully orthogonal in terms of

their inter-associations (hence the term, “semi-bipolar”). Primary motivation types and

the values that define them, found at the edges of these value dimensions may share


commonalities in their interpretation. That is, primary motivation types are not purely

liberal or conservative with regard to Openness to change. For example, from Figure 1,

the primary motivation type “Universalism” is not fully open with regard to Openness to

change; rather it would appear to have a strong relationship to Conservation as well,

especially when one moves progressively more clockwise through the motivation types2.

When evaluating cultures at the aggregate level another set of values is used.

These values are Mastery, Hierarchy, Conservation, Harmony, Egalitarian Commitment,

Intellectual Autonomy, and Affective Autonomy. The meanings of each of these values

are taken from the 1994 study by Schwartz.

Mastery emphasizes active mastery of the social environment through self-

assertion. Mastery promotes active efforts to modify one’s surroundings, and get ahead

of people.

Hierarchy is the recognition of a legitimate social ladder and related resource

allocation. Concepts related to Hierarchy would be humbleness and accepting ones place

within society.

Conservation revolves around maintenance of the status quo, propriety, and

avoiding any actions that might disturb the traditional order. Under the rubric of

Conservation falls the idea that the needs of the individual are inseparable from the needs

of the group.

Harmony emphasizes harmony with nature. Nature, in this case does not

necessarily imply the natural world of plants and animals. Rather, Nature means a world

at peace and social justice. This value stands as the antithesis of value types that promote

2 This is not covered by Schwartz directly in his 1992 or 1994 studies; rather it is an inference drawn from his works for the purposes of discussion.


actively changing the world through exploitation of people or resources and through self-

assertation. Harmony is further refined through its neutral stance with regard to

individualism as opposed to collectivism.

Egalitarian Commitment is typified by benevolence on a voluntary level for other

people. Note that this denotes a commitment that can occur among equals and not the

commitment that is represented by the value type Hierarchy.

Intellectual Autonomy is typified by freedom of intellectual choice and self-

direction. This value type, like Affective Autonomy, which is described later, represents

the degree to which a society views the individual to be entitled to pursue his or her own

individual cerebral interests or desires.

Affective Autonomy relates directly to hedonic pleasure and stimulation. As

previously mentioned it also represents the degree of importance a society puts on the

individual pursuing his or her own physical and emotional desires.

The use of a single questionnaire to explain values at two levels of measurement

could be seen as to beg the question of equivalence between the cultural and individual

level values. Schwartz (1994) does not go so far as to explain how the two structures

relate to each other at the empirical level. However, in both cases the same questionnaire

is used to generate an analysis. Further, Schwartz does allude to the use of correlation

between the two sets of primary motivations’ distances as empirical proof that the two

relate to one another. Unfortunately, he does not elucidate on this point, as such the

available literature is somewhat wanting in defining what the exact relationship between

the two levels of measurement is.


There are really only two major criticisms of the SVS. The first involves the use

of only two groups in its development. It remains unclear if using only undergraduate

students and teachers provides a representative sample of each society. However, in a

world of limited resources the coordination of two different populations across 21

different nations would probably be enough in and of itself to exhaust innumerable

research teams that have studied this scale.

Further, the SVS relies on two relatively complex forms of statistical analysis,

SSA and Oblique Factor analysis. Both SSA and Factor analysis rely on the same core

analytic technique; principle components analysis (Astill, 1998). Principle components

analysis relies on a tremendous number of respondents. Ratios of 5 or 10 respondents to

each question suggest that sample ranges must not fall lower than 280 subjects for the

SVS and really should exceed 570 for the purposes of generalizability of the factor

structure to the population as a whole (Hair et al., 1998). While the acquisition of 570

respondents does not seem unreasonable, when one considers that comparable numbers

of respondents must be acquired in two or more countries it becomes increasingly

difficult to put together a meaningful study.

Justification of Instrumentation

The prior arguments against the SVS not withstanding, several reasons to use this

instrument exist. Specifically, these arguments are that the SVS has shown itself to build

on while overcome the failings of the systems developed by Rokeach and Hofstede and

that this measure has been shown more stable in terms of its values maintaining the same

definition across cultures.


Further, the ten value types of the SVS have been shown to be nearly identical in

terms of meaning both across and within cultures. Note that this classification system

pertains to both Eastern and Western societies (Bardi & Schwartz, 2001; Schwartz, 1992;

Schwartz, 1994). This means that the internal definitions may in fact reflect the universal

values associated with the human condition and that their levels of importance vary by

the culture being measured. Evidence of this type has not been reported for Rokeach or

Hofstede’s systems.

Schwartz (1992) addresses the issue of value definitions remaining constant

among cultures through two methods: double back translation and constant interrelations

of each value regardless of the culture studied. First, Schwartz employed a rigorous

process of back translation in the process of implementing his study in each culture.

While this does not show that the definitions are necessarily the same it does show that

each culture was guaranteed to be exposed to the comparable instruments. Second, the

relationships of each value as measured through SSA remained relatively constant. That

is, each value with regard to its interrelationship with each other value remained in

largely the same position. This rigorous study of interrelationships has given the SVS the

distinction of being called the most heavily studied values scale to date (Todd & Lawson,

2003; Bilsky, 2002). When both of the factor of rigorous back translation and the factor

of a constant structure of values are taken into consideration, this tends to indicate that

each culture when exposed to the same instrument produced a highly similar profile.

The Values Scale as developed by Rokeach, Hofstede’s Values Scale, and the

SVS are to some degree interrelated. Two pieces of evidence will be given that draw

these instruments together. The first type of evidence is conceptual and relates to


Rokeach’s Values Scale. The second piece of evidence is empirical and relates to

Hofstede’s scale.

At a very basic level these scales are interrelated in that they are meant to measure

the same construct: values. However, at a more refined level these three value scales

may be seen as interrelated through their development. As previously stated, Rokeach is

seen as the father of modern values research (Braithwaite & Scott, 1991; Kelly et al.,

1972). The SVS is based on Rokeach’s work and is viewed as an extension and

refinement thereof (Braithwaite & Scott, 1991). Additionally, the SVS does not exclude

the prior research of Rokeach and Hofstede; instead it tends to build on it. The use of the

levels of abstraction with Openness to Change versus Conservation and Self-

Transcendence versus Self-Enhancement allows compatibility with Rokeach. Further, in

the validation process that Schwartz employed (1994) he compared his measure to

Hofstede’s, thus allowing both theoretical and empirical comparisons.

Empirical evidence tends to bind the Hofstede Values Scale and SVS too.

Schwartz (1994) provides correlation coefficients for his scale to that of Hofstede.

Conservatism from Hofstede’s scale is correlated with Schwartz’s Autonomy at .70.

Transcendence from Hofstede’s scale is correlated with Schwartz’s Hierarchy and

Mastery at .90. Hofstede’s individuality dimension is positively correlated with

Autonomy (both Affective and Intellectual) and negatively correlated with Conservation.

Unfortunately, Schwartz does not provide the exact level of correlation for the latter two

dimensions and does not give correlations for the remaining indices and dimensions.

However, all of the correlations reported by Schwartz are significant (p < .01).


Schwartz (1994) goes further to state the relationship between Hofstede’s Values

Survey and the SVS at the individual level. He indicated that the individual level values

for Femininity are akin to Benevolence and Universalism, Masculinity is akin to Power,

Hierarchy, and Achievement with a broad concern for self-advancement, Autonomy

versus Conformity (parallel to Individual Openness to change and Conservation) from the

Schwartz scale is equivalent to Individualism vs. Collectivism from the Hofstede scale.

Conceptual and empirical evidence has been offered that the values measures of

Rokeach and Hofstede are related to the SVS. Two conclusions may be drawn from this;

one involves the choice of instrument and the other expectations regarding what that

instrument will find. First, no answers can be found regarding the criticism of the value

scales developed by Rokeach and Hofstede. The SVS however, has been shown to not

only overcome the bulk of these criticisms but to extend prior researchers’ work. Second,

as the SVS extends both the work of Rokeach and Hofstede it can be expected that

measurements made through these two instruments will most likely lead to differences on

the SVS. Further discussion of the shortcomings of the SVS, specifically regarding its

analysis, will be withheld until later in this study as they are to be addressed in the

hypothesis section. The remainder of this introduction, leading up to the hypotheses, will

revolve around the selection of cultures to be the subject of this study.

The Difference between American, Polish, and Romanian Culture

“It is widely believed that there are marked national differences in a number of

psychological characteristics. This holds in particular for values, because the existence of

national value patterns is considered almost self-evident (Sverko, 1995).” However, such

an assumption without empirical proof could not only be considered arrogant but also


dangerous in the area of values research. That countries differ in many ways is simply a

matter of everyday observation. To be more precise, these differences seem to explain

the existence of the entities known as countries. This is particularly true of the

‘mentality’ of respective peoples, a complex set of beliefs, habits, attitudes, values, norms

and specific behavioral patterns that are attributed to them (Trentini & Muzuio, 1995).

Specifically, this section will address why it is expected that the SVS will detect

differences in Polish and U.S. culture.

Note that Romania was excluded from the prior paragraph involving cultures to

be used in this study. At the time of this study no known research has been performed on

Romanian culture with regard to values. As such, no direct evidence will be presented

regarding differences between Romania and the United States or Poland. The

justification for the use of Romania in this study is to provide contrast to measure of

values in U.S. and Poland, and to further the use of social values research.

Sverko (1995) measured the values of Australia, Belgium, English speaking

Canadians, French speaking Canadians, Croatia, Italy, Japan, Portugal, Poland, English

speaking South Africans, Afrikaans speaking South Africans, native language speaking

South Africans, and the United States using the Rokeach Values Survey. The results

were initially analyzed via hierarchical cluster analysis for which the cultures of interest

(nationality in this case was synonymous with culture; however, different languages

within the same country were considered as possibly representing different cultures) were

clustered by their value scores3. Within this analysis, Poland and the United States

3 Note that Hierarchical Cluster Analysis is an iterative (that is, step by step) process by which objects that are similar, as measured by attribute or action, in this case values, are grouped together. The sooner an object, or set of objects, falls within the same group the more likely they are to be similar in terms of the attributes that measured them.


clustered at the last of 25 steps. The stability of this cluster solution was also tested. It

was found that the agglomerative schedule was considered stable as the use of alternate

hierarchical cluster methods revealed largely the same results.4 When an F-test was used

on the resulting value scores by cluster, they ranged from 35 to 334 (all at, p < 0.0001

level) indicating broad differences in the samples on each value.

This study also examined the alternative hypothesis that clustering was occurring

based on language alone as opposed to differences based on larger cultural issues. This

alternative hypothesis was not supported as English speaking and French Canadians

clustered within three steps while the various language speakers of South Africa clustered

within eight steps (one step for English and Afrikaans speaking South Africans, eight for

Native speaking and the English-Afrikaans speakers). Further, double back translations

were used to insure that the survey was properly translated, thus eliminating or at least

reducing bias associated with the language in which the survey was delivered. This study

offers evidence that differences will be found between Poland and U.S. on the SVS. As

previously discussed Schwartz based the SVS on Rokeach’s research. If the relationship

between Rokeach’s Values Survey and the SVS hold then one reasonably expect the SVS

to reproduce these results.

Scores from Hofstede’s Values Survey for Poland and the United States are

summarized in Table 3 of this study as obtained from a study by Braithwaite and Scott

(1991). Note that the values obtained for Poland are based on estimates derived from a

student sample while the sample from the United States was derived from the same

international corporation as the majority of the validation procedure. Poland was normed

4 Sverko (1995) did not present the alternate solutions in this case. As such, they are not discussed in this literature review.


against the Canadian sample through equivalent sampling techniques. The largest

differences are found in the realm of Individualism versus Collectivism and Uncertainty

Avoidance. Not enough information is present from the existing sources to perform

significance testing (Middleton & Jones, 2000). However, evidence has been given

showing strong correlations between the SVS and Hofstede’s Values Survey, suggesting,

again, differing values profiles for the two nations. If statistical testing were available for

this one could no doubt be more certain; however, none are available for this particular

study.

A stronger set of evidence is presented by Robert, Probst, Martocchi, Drasgow, &

Lawler (2000). In their study, Hofstede’s Values Scale was used along with the statistical

testing in the form of structural equation modeling. Note that the Individuality versus

Collectivism scale was mixed with the Power Distance scale to form Horizontal

Collectivism, Horizontal Individualism, Vertical Individualism, and Vertical

Collectivism. High Power Distance represents vertical societies while low Power

Distance Represent horizontal societies in this case. Individualism versus Collectivism

retained their original meaning. The results of this study are summarized in Table 4.

Further, Hofstede’s Individualism versus Collectivism scale has been shown to

correlate with Gross National Product on a per capita basis at .87 for teachers and .81 for

students at the p < .01 level (Schwartz, 1994). At the time of this writing, the Gross

National Product, per capita, for the United States is $36,300 (Central Intelligence

Agency, 2003b), for Poland this figure is $9,500 (Central Intelligence Agency, 2003a)

and within Romania this figure is $7,600 (Central Intelligence Agency, 2004). This

means that if the relationship between per capita Gross National Product and


Collectivism versus Individualism remains intact, that levels of Collectivism in the

United States, Poland, and Romania would be projected to be different. This should lead

to differences on the semi-bipolar dimensions of Self-Transcendence versus Self-

Enhancement on the SVS.

Perhaps the strongest piece of evidence that a differential in the values profiles

exists between the U.S. and Poland is offered by Schwartz himself. The United States

has been found to be high in Mastery under Schwartz’s framework and not especially

high on Affective Autonomy and Conservation. By contrast, Poland has been found to be

higher in Affective Autonomy than the United States and high overall in Conservation

(Schwartz, 1994). A full listing of the cultural level values profiles for Poland and the

United States under the framework of the SVS is presented in Table 5 of this study. Note

that these measurements were performed at the cultural level using the SVS. However,

recall that regardless of level of measurement the same questionnaire is used to measure

values under Schwartz’s model.

Hypotheses

So far this study has suggested that values are an integral part of culture.

Additionally, it has been suggested that despite some shortcomings, the SVS is the

instrument of choice in measuring values. Further, a variety of evidence indicates that

Poland and the United States are different with regard to their values profiles. Romania

remains unexplored in any study. This study seeks to address the shortcomings of SVS,

specifically its reliance on principal component based analyses. Fortunately, other

methods exist to analyze this data outside of SSA and oblique factor analysis.


For instance, Todd & Lawson (2003) measured the values of frugal consumers

with the SVS and the interrelationships of its items via PROXSCAL. The findings of this

study supported a refinement the model in which the primary motivations of Conformity

and Tradition were merged into one classification. Unfortunately, this study did not use

all the items present in the full SVS. Additionally, only respondents from one culture,

citizens of New Zealand, were used. It remains to be seen if the interrelationships of the

items that compose the SVS as measured through MDS remain constant across cultures.

Smallest Space Analysis and oblique factor analysis rely on correlational analysis;

MDS is based upon dissimilarity between stimuli and is different. Specifically, MDS

relies on a structure of interrelationships between items while factor analysis relies on

covariation. It remains unclear if the primary motivations as specified by Schwartz

(1992, 1994) can be measured via dissimilarity based MDS.

From here a hypothesis may be formed:

Hypothesis 1: Values as measured by the SVS will maintain the same structure as

measured in prior studies regarding their primary motivations in the United States,

Poland, and Romania when using dissimilarity based MDS methodology.


measured in prior studies with regard to the semi-bipolar dimension of Conservation

versus Openness to Change in the United States, Poland, and Romania when using

dissimilarity based MDS methodology.


measured in prior studies with regard to the semi-bipolar dimension of Self-


Transcendence versus Self-Enhancement in the United States, Poland, and Romania

when using dissimilarity based MDS methodology.


Method

Respondents were recruited from three countries: the United States, Poland, and

Romania. Note that all respondents were obtained through convenience methods. That

is, friends, family, and contacts of friends and family groups were contacted and asked to

fill out each country’s survey. A total of 136 United States respondents, 266 Polish

respondents, and 258 Romanian respondents were recruited through this method. The

primary thrust of the data collection process was to obtain as many respondents as

possible regardless of subgroups with the caveats of not recruiting children, the

imprisoned, or the mentally ill. Specifically, as long as a respondent was 18 or older he

or she could be of any age, occupation, level of education, income level, or race. Note

that for reasons which will be discussed in the analysis portion of this study, listwise

deletion of respondents was imposed on the resultant data set. By listwise deletion, it is

meant that if a respondent failed to answer one question within the variables of interest

(for the purposes of this analysis the demographics sections and the SVS) he or she was

eliminated from the data set as a whole. This resulted in a final data set with 98 United

States respondents, 201 Polish respondents, and 128 Romanian respondents.

The surveys used in this study contained not only the SVS but also other

questions not directly related to measurement of values5. Outside of the SVS, the surveys

contained different items by country. This means that three separate instruments were

used. For instance, the Polish and Romanian survey contained the Marlowe-Crowne

5 It should be noted that one of the sections of the Polish survey contained the Marlowe-Crowne Social Desirability Scale (MCSDS) and the Social Desirability Scale-17 (SDS-17). The original intent of including these scales was the validation of social desirability measures outside the countries of their origin. However, the manipulation failed. The results of a MANCOVA, Multiple One-Way ANOVAs, and Logistic Regression showed no, or minimal differences between the fake good and normal groups on not only the MCSDS and SDS-17 but also the SVS. As such both of these groups were combined to form the Polish sample.


Social Desirability Scale and Social Desirability Scale-17 while the American survey did

not. Copies of the full questionnaires, in English, may be found in Appendices B, C, and

D of this study. Demographics were also included in both studies. The use of these

demographics will be addressed in the Analysis portion of this study, as they will be used

as statistical controls for non-representative samples.

Originally, all three of the instruments for this study were composed in standard

United States English. Once final versions of these instruments were completed, a

rigorous double back translation process was implemented. Specifically, the respective

questionnaires were translated into Polish and Romanian and back into English. Items

differing significantly in their content upon back translation were then taken from their

original English and translated again. This process was repeated until all of the items

were translated with the appropriate content. Different translators were used at each step

of the process to avoid memorization or learning of the items. Side by side comparisons

of the Polish and Romanian versions of the SVS and demographics along in their

respective languages as well as their original English versions and English back

translations may be found in Appendices E and F of this study.

Note that this study does not use the full 57 item SVS. The 57th item pertaining to

Self-Indulgence was excluded as it was not present in the original study that Schwartz

(1992) used to validate his instrument.

Analysis

For the purposes of discussion, the analysis of the questionnaires’ results may be

separated into four phases. The first phase involves the alignment of demographic

questions for later statistical control, the elimination of respondents who provided


incomplete data, and the selection of variables for demographic control. Phase Two

involved the statistical control of demographics. The third phase centers around the

production of Multidimensional Scaling Models (MDS). Phase Four involved the

analysis of the primary motivation types within the MDS space as described previously

by the Schwartz model through Multiple Discriminant Functions (MDF) and binary

logistic regression.

Phase One

Demographic questions from all three countries were first standardized in terms

of their name and coding within the data set. The next portion of this phase involved the

elimination of respondents who did not answer all of the SVS or the needed demographic

questions. At two points in this analysis statistical techniques were used that necessitated

listwise deletion, controlling for demographics and the measure of association used in the

production of the MDS models (covered in Phases Two and Three of this study

respectively). The results of the demographic questions common to all three surveys

were then compared with the purpose of selecting the variables which would offer the

broadest span of statistical control yet also leave the largest group of surviving

respondents with regard to missing data.

Phase Two

As previously stated the recruitment phase of this study involved finding as many

respondents as possible, regardless of demographic background, to fill out this study’s

questionnaires. This resulted in samples that were large enough for complex statistical

testing but not necessarily representative of the populations of the countries of interest.

As such, a statistical control was needed to eliminate the effects of these differing


backgrounds with regard to the results of the SVS. While the SVS was produced with the

intent of the showing a universal values structure not only within but also between

nations, studies have shown differences associated with demographics (Lee, 2003; Na &

Duckitt, 2003; Feather, 1979). The decision was therefore made to control for

demographics within each sample.

The technique used to control influence of demographics was Multiple Linear

Regression. Specifically, the dependent or predicted variables would be each of the 56

items of the SVS. The independent or predictor variables would be the demographics.

Standardized residuals from this process would then be used in place of each item.

Note that in the case of this study the impact of demographics on the SVS’s scores within

cultures was not of interest. Rather this study’s thrust was the measurement of values and

their differences in importance between cultures. Previous studies have used this

technique under similar circumstances (Blake & Neuendorf, in press).

The theoretical underpinnings of this process are detailed as follows. When

plotting a linear regression line a line of best fit is placed within the plot of the data.

Each predictor variable adjusts the slope of this line and in so doing accounts for the

predicted variable’s variance. The space remaining between each data point and this line

is called the residual. This distance represents the variance unaccounted for by the

regression analysis. Therefore, in this study demographics were used as independent

variables and SVS scores as dependent variables; the residuals were SVS scores with the

influence of demographics subtracted. Specifically, in the case of this study, “controlled”

means that the residual scores of this regression are adjusted for linear effects assuming


that there are no interactions among the demographics. The residuals from this portion of

the analysis were used in place of the raw data for the remainder of the analysis.

The regression models used the SPSS algorithm. Residuals may be produced

only for subjects with data for the independent variables (demographics in this case) in

SPSS. This along with analysis hurdles discussed in Phase 3 of this study necessitated

listwise deletion of respondents with missing data.

Specifics of the models used are as follows. The demographics (the independent

variables) were entered in a block at single step. This ensured all of the demographics,

regardless of their strength of association with a particular SVS score (the dependent

variable), were entered into the equation. Note that the variables used did not vary by

country. That is, if age were to be used in the United States model it would be used in the

Polish and Romanian models too.

A separate series of regressions were calculated for each national sample. Hence,

the regression coefficients for a given predictor could vary from country to country.

Phase Three

The next phase of this analysis involved the production of a Weighted

Multidimensional Scaling Model (WMDS) that would produce a common space based on

all of the variance from all of the countries used. Classic Multidimensional Scaling

(CMDS) models were also to be constructed for each country individually. As two forms

of MDS are used in this analysis, two separate designations are employed for the

different techniques. CMDS refers to Multidimensional Scaling it’s most basic form,

scaling of a single data matrix. WMDS refers to the technique that combines multiple

data matrices to produce a common space that attempts to amalgamate all of the data


therein. Note that one of the intents of this study is to allow the interpretation of the SVS

through SPSS as the data analysis platform. ALSCAL is the software that is used in

SPSS to produce many of the types of MDS models. As such, from time to time

allusions to ALSCAL as a software platform will be made. A brief description of both

CMDS and WMDS is given here to insure that readers have the common background to

understand these analyses as applied to the current study. However, if the reader is

foreign to these statistics two good sources to begin familiarizing oneself would be Hair,

Tatham, and Anderson (1998) and Myers (1996).

CMDS is a statistical process by which the interrelationships between multiple

variables may be examined. Specifically, measures of similarity or dissimilarity are

employed for each unique pair of variables that the researcher wishes to examine. That

is, if four variables are to be examined then six pairings of variables would be required; if

five variables are to be examined ten pairings of variables would be required; and so

forth. Typically, this measure is a rating of perceived similarity. The only restrictions to

these measures of similarity are that they are at least ordinal in nature. For some cases

Pearson Correlation Coefficients (r) are used; however, this study employs the use of

different coefficient of similarity for reasons to be discussed later.

These indicators of similarity produce a matrix of intervariable similarities.

Drawing from the previous examples, if four variables are to be studied via MDS a four

by four matrix of dissimilarity scores is produced is produced. This produces 16

distances in a matrix that are symmetrical about a dividing line of perfect similarity

(perfect similarity in this case is signified through zero distance). This matrix is then

used to determine a set of coordinates on a number of dimensions specified by the


researcher. These dimensions are for a hypothetical space and often produced as a

graphical readout. The researcher then examines these coordinates, or the resulting plot

of interpoint distances, if the results to be in the form of a graphical readout, hoping to

discern the variables similarities. Typically, CMDS is performed through an iterative

process by which a measure of variance relating both the original measure of

dissimilarity and the locations of the variables on the new set of dimensions is produced.

(Iterative in this case means a series of steps that are repeated. Each singular iteration, or

pass through the instruction set, is one step.) In fact, one of the outputs of the CMDS

algorithm is variance shared between to two sets of scores. Generally, this variance

coefficient is similar to r2 with modifications for the specific algorithm. The

interpretation of the variance coefficient produced by most CMDS software is the same

as it would be for correlation squared. That is, a variation shared coefficient between a

matrix of distances and the resulting CMDS output of .8211 would mean that 82.11% of

the variance recorded in the scaled dissimilarity matrix may be found within the

generated interpoint distances.

Another measure that is also generally employed measures the level of distortion

within this hypothetical space: Stress. A basic thought exercise explains the use of stress.

Pretend that there are distances for three points that are known and coordinates for these

three points are to be generated in two dimensions. Points one and two are ten inches

apart. Point three is four inches away from point one and two. The results of these

distances cannot be mapped in standard Euclidean space. Stress is the level of distortion

existing in the space by the algorithm attempts to accommodate these distances. Note

that standard Euclidean space is sometimes used; however other rules are sometimes used


to define these spaces. The CMDS algorithm works through various configurations of

these points to minimize the stress of resulting coordinate set. Stress, like variance

shared, varies between zero and one. With zero indicating no distortion within standard

Euclidean space, or whichever other metric is being used, and one indicating complete

and utter distortion.

In most algorithms, there is an indirect relationship between variance shared and

stress. Stated another way, by repeating the steps CMDS are typically iterative processes.

The default maximum iterations available for these algorithms are 30 in SPSS. This

limitation is arbitrary. The main reason for this limitation is to conserve processor time

and is an artifact of the days when computing was performed on relatively slow

mainframe computers. For the purposes of this study all MDS algorithms were set to the

maximum number of allowed iterations, 999.

Other measures that are used in MDS are S-stress convergence and minimum S-

stress value. S-stress is squared stress, which is computed under a slightly different

formula than stress though its interpretation remains largely the same. S-stress is

calculated to gauge the minimum change in S-stress necessary to terminate the algorithm.

That is, when a change between iterations of S-stress is encountered lower than the set

level the algorithm will stop and deliver the derived coordinates. The default value for

convergent S-stress in SPSS is .001. Like the maximum iterations allowed this number is

arbitrary and was changed to .0001 (the minimum allowed through SPSS) to generate

solutions with lesser degrees of stress.

Minimum S-Stress is the lowest amount of stress that will be measured in the

iterative MDS process. This is a way of measuring the level of precision involved in the


analysis. The Minimum S-Stress measure, like convergent S-Stress and Maximum

iterations, are set to arbitrary levels. The default for Minimum S-stress in SPSS is 0.005.

For this study, Minimum S-Stress will be set to a lower level to increase the precision of

the findings. This study will use .0001, the lowest value allowed by SPSS.

WMDS is used when two or more data sets are used to generate coordinates in a

hypothetical space where this set of coordinates is meant to represent a model that fits

these data sets as a whole. WMDS uses largely the same process described for CMDS.

However, instead of fitting the data from a single matrix to a hypothetical space, multiple

matrices are used. The hypothetical space that WMDS finally produces is referred to as

“common space.” The term common space is used as this space demonstrates the

variation present in all the data matrices used to develop it. Several measures are used in

WMDS that are relevant to only this process. Specifically WMDS makes use of

weirdness, importance, and subset weights. Note that r2 and stress are still produced for

each individual matrix and still fall under the same rubric of interpretation as with

CMDS.

Weirdness is named relatively intuitively. This measure shows how divergent a

given matrix is from an arbitrary midpoint of normalcy. Note that Weirdness varies

between 0 and 1. A Weirdness score of 0.00 between two data sets means that they are

effectively in total agreement or geometric congruence. A Weirdness score of 1.00

means that the two data matrices are in total disagreement.

Another measure specific to WMDS is Importance. The Importance measure is

intuitively named too. This measure shows the relative importance of a given dimension

with regard to explaining interpoint dissimilarities. Like the weirdness measure, the


importance measure varies between 0 and 1. An importance score of 1 means that a

given dimension explains all or nearly all of the given data sets’ variance. An importance

score of 0 means that a given dimension is irrelevant or nearly irrelevant when evaluating

a given dimension.

Subject weights provide the basis for converting the common space to an

individual space for each subject (that is, data matrix, here the nation sample). A weight

is generated for each dimension for each “subject”; these weights are combined with the

coordinates of each stimuli multiplicatively (more specifically, the square root of the

weight) to obtain coordinates of a stimuli’s value on the dimension in question in the

subject (in this case, nation sample) space.

Note that solutions will be generated for one to six dimensions using CMDS and

two to five dimensions using WMDS (these are the full range of solutions available

through SPSS). Selection of which models will be used will be addressed after

descriptions of each model are discussed.

Many variations exist on the basic theme of developing these hypothetical spaces,

such that differing algorithms may produce different levels of variance shared, stress, and

hypothetical coordinates. However, the algorithms employed are generally stable in that

they will always produce the same levels of variance shared, stress, and hypothetical

coordinates for each individual data set.

The data input to MDS is a matrix of inter-value dissimilarities (or similarities).

In this study, the similarity is gauged by correlations between ratings of the perceived

importance of the stimuli (value statements). Very often when one speaks of correlation

in the social sciences he or she is referring to Pearson’s Correlation Coefficient (r).


Pearson’s r has enjoyed an incredible amount of use in the social sciences. However, the

coefficient of choice for this study was Lin’s Concordance Coefficient, which will be

referred to as rlc. The formula for rlc may be found in Figure 2. An introductory

discussion of rlc may be found in The Content Analysis Guide Book (Neuendorf, 2002)

while a more technical discussion is contained in Lin (1989). This study will give a brief

discussion of and justification of its use within the performed CMDS and WMDS

analyses.

Specifically, consider the data sets presented in Table 6. Additionally, consider

the scatterplots provided for this data in Chart 1. As indexed by r, the association for

variables X and Y would be 1.00 (p < 0.001). Next consider the correlation between X

and Z. Again, the correlation when calculated through r is 1.00 (p < 0.001). That is,

through the use of r there appears to be no difference in the linear associations of these

two pairs of variables. However, when examining this data it becomes clear that there is

a constant associated with variable Z that tends to elevate its scores two units (in this case

the unites are arbitrary but common to all three variables and assumed to be

interval/ratio). Lin’s Concordance Coefficient considers this distortion but still has many

of the same properties as r. Specifically, the same levels of significance are used for rlc

as r. Additionally, just as r varies between -1.00 and 1.00 so does rlc. The interpretation

of the two coefficients are largely the same with anchors occurring at -1.00, a strong

negative relationship, 0, no relationship whatsoever, and 1.00 a strong positive

relationship. Finally, rlc and r require the use of minimally interval level data. As such,

rlc, for the hypothetical variables mentioned earlier, X and Y, would still be 1.00 (p <

0.001). However, rlc for variables X and Z would be 0.805 (p < .01).


This exercise is important when considering the use of correlation coefficients as

a measure of similarity between two stimuli. Two items can be very different (as in X

and Z in Chart 1), but still show a high r. Lin’s Concordance Coefficient is not prey to

this problem to the same degree as it (unlike r) adjusts for elevation differences.

In this case, which may be generalized to other situations involving the linear

association between variables, rlc takes into account the issue of elevation. That is, X and

Z’s relationship is perfect but is elevated two units by comparison to X and Y. As such,

rlc is superior to r as it does not produce a figure that indicates a perfect correspondence

when one does not exist.

Unfortunately, programs that calculate rlc were not easily available at the time of

this analysis. As such, this formula for rlc was programmed into Microsoft Excel as a

formula and the association matrix needed for the later CMDS and WMDS models was

generated there (Figure 3 shows the Excel formula used for this computation).

Unfortunately, Excel does not support pairwise elimination of subjects with missing data.

This hurdle, as well as the previously mentioned use of residuals, necessitated the use of

a listwise elimination paradigm for subjects with missing data.

Whether r or rlc is used a basic transform is need to convert the measure of

association to dissimilarity. That is, both of these coefficients revolve around the

assumption that 1 is shows perfect agreement, 0 no association, -1 perfect disagreement.

If two objects are extremely similar, closer to 1 when interpreted through r or rlc, one

would expect them to be closer together than two objects which are extremely different

closer to -1 when interpreted through r or rlc, under the rubric of MDS in general.

Therefore, a transform is needed to convert rlc to dissimilarity where 0 is no dissimilarity,


1 is a fairly large dissimilarity, and 2 is the maximum dissimilarity possible. The

transform used in this study is shown in Figure 4, while its Excel equivalent is shown in

Figure 5. Note that this transform is in line with typical transforms used to change

coefficients of similarity to distances representing dissimilarity when using an MDS

algorithm presented in Cox and Cox’s Multidimensional Scaling (2001).

Phase Four

Schwartz’s theory as demonstrated in Figure 1 assumes that arbitrary lines

separate the primary motivation types by the values, which populate them. Neither

CMDS nor WMDS are able to generate these artificial borders. An objective measure is

needed to judge whether the maps produced through CMDS and WMDS can be separated

into realms based on variance demonstrated through these models. Further, to maximize

the amount of variance represented by the CMDS and WMDS models hyperspace

dimensions will most likely be used. Hyperspace refers to dimensionalities, which

exceed the standard three dimensions used by people in everyday life. While exceeding

three dimensions will most likely account for a larger portion of the variance available in

the original matrices of relationship discussed earlier, a limitation is encountered, as

analysis via straight viewing is no longer possible. Again, an objective process or

processes are needed that analyzes the relationships of the values in a space that cannot

be typically comprehended by simple human observation. As such, the final phase of the

analysis involves the use of a Multiple Discriminant Functions (MDF) and binary logistic

regression to look into the resulting hypothetical space. MDF will be used in this study

to determine how well the Schwartz model is maintained regarding the ten overarching

value types for each country. Binary logistic regression will be used to evaluate how well


the dichotomies of Self-Enhancement versus Self-Transcendence and Openness to

Change versus Conservation are predicted by the Schwartz models. This study will

provide a brief discussion of MDF and binary logistic regression as several unique issues

are presented in the case of this study. However, if the reader is unfamiliar with this form

of analysis he or she should seek out an introductory multivariate text for a more

complete description of these statistics (again, Hair et. al., 1998) is indispensable in this

regard).

MDF is a statistical analysis that uses a determinance model to predict categorical

group membership from parametric data by use of canonical functions. To clarify, a

determinance model is a model that uses a series of variables to predict another variable.

Examples of determinance-based models include multiple regression and binary logistic

regression. The essential attribute of determinance-based models is that commonalities

between the independent variables (whether standardized in the case of variance or

unstandardized in the form of variation) are entered in series to predict the dependent

variable. Second, the independent variables used in this analysis can be continuous or

categorical in nature while the dependent variable is usually categorical in nature. Third

the data is fed through a canonical correlation process to produce functions, which

differentiate between the groups as specified by the dependent variable.

MDF produces loading of each variable on the functions that are used to

discriminate between the categorical level variable. Typically, these coefficients are of

great interest to researcher as they tend to indicate the relative impact of predictor

variables in differentiating between categories of the predicted variable. However, in this

case, the predictor variables will be the dimensions from various CMDS and WMDS


solutions. The composition of the CMDS or WMDS dimensions are inferred from the

distances developed from the original data set, in this case via Lin’s Concordance

Coefficient with regard to the SVS items controlled for demographic influence. This

leads to a difficult interpretation of what these loadings will mean, as the CMDS and

WMDS dimensions are developed based on the distances.

Another output of the MDF are the centroids associated with each category of the

dependent variable. These centroids are the arithmetic average of the function scores for

each category. That is, a score on each function is developed for each category. Each

category in this case is the primary motivation type that acts to classify individual values.

The implication is that these centroids are the most typical individual value for each

primary motivation type. However, whether the position of each centroid corresponds to

a value which may be interpreted as something meaningful is debatable. For instance,

what would one call a value that is two units away from clean but only one unit away

from national security? The result is that these centroids will additionally be very

difficult to interpret. Fortunately, the farther apart these centroids are from one another

the better the discrimination offered by the MDF model, and this type of data regarding

the model can be interpreted.

The next output of MDF is the level of significance offered by each function. The

practical significance level of this analysis is suspect. The input to the MDF model is an

abstraction of value measures as derived from the various CMDS and WMDS solutions

who themselves are derived of a theoretically limited population (the SVS scores). To

clarify, Schwartz (1992) states that his values measure is exhaustive or near exhaustive in

terms of the values that are measured. The question therefore becomes to what are


models to be generalized? Is this a generalization to a finite, relatively small set of

values? Is this a generalization to an abstraction, via CMDS or WMDS, of a finite,

relatively small set of values?

This study takes the view that this method of analysis presumes the latter as

opposed to the former paradigm. Specifically, the CMDS or WMDS solutions will only

generalize as well as the variance it accounts for. However, care must be put forth in

regard to which MDS solution best represents the data at hand.

The final output of MDF is a hit and miss table that measures the number of

objects (in this case values) correctly classified through the MDF formula. Typically, a

derivation of Chi-Squared ( 2) called Press’ Q is used to evaluate the overall significance

of the resultant classification scheme (Hair et al., 1998). However, this is a special

circumstance in that 10 categories will be used to classify 56 objects. This suggests that

the minimum number of objects per cell will fall below the minimum for a 2 formula.

To clarify, Press’ Q evaluates the likelihood of the MDF model is classifying the

individual values at a rate above chance. However, the assumptions on which Press’ Q

are based may be violated.

While it could be argued that data in this case are being collapsed from the full

array of subjects, it could also be argued that there are simply not enough values to make

the determination of the model’s overall divergence from chance. This study will

therefore use two measures, Press’ Q and Cohen’s Kappa. Cohen’s Kappa ( ) was

originally developed for methodologies such as ethnography and content analysis, as an

alternative to percentage agreement. Typically, it is used when one wishes to judge the

level of agreement beyond chance between two coders when coding the same variable


with the same stimuli (typically behavior of a human or animal). This study seeks to use

Cohen’s Kappa to judge the percentage agreement beyond chance between Schwartz’s

(1992) theoretical model and the current data. Note that Cohen’s Kappa varies like a

normal percentage, falling between 1.00 and 0.00. That is, a Kappa value of 1.00

indicates total agreement and while a Kappa value of 0.00 indicates total disagreement

(Neuendorf, 2002). The conceptual formula along with an example data set is provided

in Figures 6 and 7 respectively; the Excel formula employed in Kappa’s calculation is

available in Figure 8. As a baseline the basic percentage agreement between Schwartz’s

(1992) theoretical model and the present data as analyzed by MDS are also provided.

Further, this analysis report will use binary logistic regression to look into the

CMDS and WMDS models. Binary logistic regression is similar to MDF in that both are

determinance based and both rely on the dependent variable being categorical in nature.

Binary logistic regression differentiates between categories of a dichotomous variable.

The variables used as predictors in binary logistic regression may all be categorical in

nature. Once again, this study will provide a brief overview of binary logistic regression

and its unique application in this case. However, as binary logistic regression is complex

in and of itself much less in this unique application, readers unfamiliar with this statistic

should familiarize themselves with its methodology and implementation via an

introductory multivariate statistics text.

Binary logistic regression relies on adjusting the position of the function of a

natural log (an S-shaped function) in differentiating between two categories of a

dichotomous variable. Each independent, or predictor variable, adjusts the position of

several S-shaped functions until an optimal solution is reached. The caveats discussed


regarding MDF still apply to this statistic as the output is very similar. However, the

conceptualization of this statistic is much easier to understand with regard to how it

applies to the various hypothetical spaces developed by the CMDS and WMDS models.

Specifically, the S-shaped functions are used to differentiate between the two categories

of interest. These functions would serve as dividing wall in the hyperspace solutions.

They would be the equivalent to hyperspace barrier separating the two categories of the

MDS (CMDS and WMDS) models arrived at in Phase Three of this study.

An oddity of this study that must discussed regards prediction with regard to

MDF and binary logistic regression. MDF and binary logistic regression are usually used

to predict group membership. These predictions are then compared to the actual

classification results. In this case, these statistics are being used to interpret a

hypothetical space. As such, the results of these statistics with regard to this study

become the actual results. While the Schwartz model, the model to which these results

will be compared, become the predicted results when interpreting the resultant

classification tables. To avoid confusion this study will use the label Empirically

Expected when referring to results predicted under the Schwartz (1992) model and

Observed for the results of the statistical analyses of this study.

The final portion of this analysis seeks to measure the levels of similarity between

the interrelationships of the SVS’s value statements between each country. To this end

the raw coefficients of relationship for each value interrelationship will be measured

between the United States, Poland, and Romania. That is, the rlc value for the first item

of the SVS “Equality” will have been compared to the second item “Inner Harmony” will

be compared between each nation. Each unique pair’s concordance coefficient will be


used as the data for a coefficient of relationship (this will be the values as developed from

the 56 by 56 item matrix described in Phase 2 of this analysis). The requirements of this

analysis suggest that not only could the rlc values be measured but also the distances as

developed in through the CMDS algorithm. The distances developed through CMDS are

interval/ratio in nature. As such, Pearson’s Correlations Coefficient (previously defined

as r) may be employed in this case. However, the rlc values are of an ordinal nature.

This means that Kendal’s Tau-b will be used6. Kendal’s Tau-b will also be used with the

rlc values allowing comparisons between the original set of concordance values as well as

the CMDS derived coordinate sets.

This portion of the analysis will rely on only one half of the concordance matrices

previously mentioned. That is, only a triangular matrix under the main diagonal of

perfect correlations will be measured. In the case of the dimensions generated via CMDS

an extrapolation of the Pythagorean Theorem will be used to generate interpoint

distances7. Note that this will extrapolation assumes squared Euclidean distance;

however, this analysis will use Euclidean distance.

Results

Just as in the Analysis portion of this report the Results section is divided into

four phases. The first phase involved the alignment of demographic questions for later

6 Note Kendal’s Tau-b measures the number of inversions relative to rank order while compensating for a large number of ties. This statistic measures the number of times one score occurs before another when comparing two data sets and is conceptually similar to Gamma, Spearman’s Rho, and Pearson’s r (Brewer n.d.). The other possible statistic would have been Spearman’s Rho. Spearman’s Rho was rejected as this statistic as it was unknown what the number of ties was in the data set and Kendal’s Tau-b tends to be more appropriate when the data is more cross-tab like (Emerson n.d). 7 The classic Pythagorean Theorem is meant for two dimensions and may be illustrated as A2 + B2 = C2. Where the variable A represents the position of the variable on dimension one and B represents the position of the variable on dimension two. The extrapolation in this case is for six dimensions and may be illustrated as: A2 + B2 + C2 + D2 + E2 + F2 = G2.


statistical control, the elimination of respondents who provided incomplete data, and the

selection of variables for demographic control purposes. Phase Two involved the

statistical control of demographics. The third phase centers on the production of MDS

models. Phase Four involved the analysis of the primary motivation types within the

MDS spaces as described previously by the Schwartz model through Multiple

Discriminant Functions (MDF) and binary logistic regression.

Phase One

Appendix G of this study gives the SPSS Syntax used to align the demographic

questions from the three countries surveyed. Note that Syntax related to Income

questions from all three countries is included though this question was eventually

excluded. At this point in the process no screening of demographic variables had taken

place with regard to their feasibility and missing data. A summary of the demographics

for the respondents used in this study is available in the 3rd, 4th, and 5th columns of Table

9.

A series of trade offs were made with regard to which demographics were likely

to best filter out the influence of non-national background and which demographics

received such a poor response rate such that they would hinder further analysis. Details of

this process are available in Tables 9, 10, and 11 of this study.

Note that multiple models were considered with regard to the elimination of

demographics. However, the variables with most missing data were household size and

income. The choice was made to eliminate these two variables as otherwise their

inclusion when applied listwise deletion issues discussed in the analysis portion of this

study would have produced too small a set of respondents for analysis.


Phase Two

Recall that Phase Two of the Analysis involved the dummy coding of a select

group of demographics to control for non-representative sampling. Categorical variables

such as level of education and occupation type were dummy coded while age due to its

ratio nature was left in its original format. A full breakdown of the dummy categories

and their univariate descriptors is given in the first two columns of Table 9 of this study

(it is suggested that the reader also view the instruments employed, in their original

English, appropriate language, and English back translations, presented in Appendices H,

I, and J for the United States, Poland, and Romania respectively). Each country had its

own regression model. That is, separate models were employed using the same variables

for each country. The use of separate models was necessary as it was unclear if each

nation would be effected differently by the demographics available (see Blake and

Neuendorf (in press) for an alternate perspective). Further, the same demographic

questions from each country were used. That is, religion was asked in Poland but not in

the United States. Therefore, as it is unclear what effect religion has in both Poland and

the United States, it was excluded from the regression model. Note that the prototypes of

the SPSS syntax employed in this analysis may be found in Appendix K.

A total of the 168 regression models were employed in this study (56 for each

country). As the inclusion of all 168 models would provide little usable information

regarding this study’s hypotheses and grossly inflate the size of this report a sample of

the regression models is available (in Tables 12 through 17). Further, certain statistics

common to all of the regression models specifically the degrees of freedom, and


collinearity diagnostics, for each country’s overall model set are provided in Tables 18

and 19 respectively.

Note that the overall models rarely achieve significance. This suggests the low

variance accounted for relative to the number of variables acting as predictors indicates

little predictive power of the demographics. Differences among individuals in their

residual scores should be comparatively free of even these modest effects of respondent

demographics.

Phase Three

Recall that to implement an MDS analysis coefficients of dissimilarity must be

created for each variable to every other variable. As previously discussed the coefficient

of choice for this analysis was Lin’s Concordance Coefficient. The resultant distributions

of concordance ratios for this process are described by country in Table 20. Note that the

overall distributions are roughly normal when measured by the Skewness and Kurtosis

measures. Further, note that the ranges of Lin’s Concordance Ratio vary within a range

of 0.813 and -0.413. Considering that this statistic penalizes relationships in which the

variables are differentially elevated, it could be inferred that there are several strong

relationships between the value measures.

Note that the SPSS Syntax used in the production of the both the WMDS and

CMDS solutions is available in Appendices L and M of this study.

Initially, a WMDS solution was attempted that would consider the data from all

three countries, the United, States, Poland, and Romania. The resulting R2 and stress

coefficients may be found in Tables 21 and 22, respectively. Scatterplots of these

statistics are available in Charts 2 and 3 respectively. Weights, Weirdness, and


Importance statistics may be found in Tables 23, 24 and 25, respectively. For reasons of

reproducibility, the number of iterations used by ALSCAL to produce these solutions

may be found in Table 26.

The variance shared and stress coefficients are of particular concern. Overall, the

commonality among the three data sets (matrices) is lower than anticipated. Typically,

for an MDS solution to be considered of value, variance explained would need to exceed

0.80 or 0.90 as a guideline (Myers, 1996). When averaged, none of the solutions offered

exceeds this mark. Of further concern is that the variance explained tends to be

dominated by one or two countries for each dimension of the solution. The most unusual

outcome of this analysis is that the five dimensional solution offers a lower amount of

variance explained than the four dimensional solution. Overall, it seems that the Polish

data set is dominating the analysis in terms of variance explained.

The weights for each country by dimension offer more evidence of instability.

Specifically, it appears that in all of the solutions except the five dimensional one, that

each country dominates a separate dimension. That is, for the two-dimensional solution

an even mix of dominance is held by the U.S. and Romania, while Poland dominates the

second dimension. In the three-dimensional solution, Romania holds sway over

dimension one, while the United States and Poland control dimension two. Poland, in the

three dimensional solution, holds sway over dimension two. This effect is even more

profound in the four dimensional solution, where Poland controls the first dimension,

Romania the second, the United States the fourth, and the third dimension is a somewhat

even mix.


The Weirdness coefficients tell an even more interesting story. Overall, the

highest Weirdness ratings are associated with the four dimensional solution, the one with

the highest variance explained. While the three and five dimensional solutions are about

even in terms of their levels of Weirdness, the two-dimensional solution, the one with the

lowest variance explained, offers the lowest marks on this index.

Overall, these indicators could be taken to indicate that ALSCAL had trouble

arriving at a WMDS solution that could push the requirements of three data sets together.

The best solution when one considers all of the available indicators would seem to be the

five dimensional solution. This solution offers the highest mix of dominance when

comparing country to dimension, lower levels of stress overall, and lower weirdness

indicators by country. However, this solution has its pitfalls too. This solution offers the

lowest representation of the Romanian data set both in terms of variance explained and

stress.

Alternate solutions were arrived at via CMDS. Three separate models were

developed, one for each country. Note that when considering this methodology,

comparisons in terms of distance cannot be made between countries. That is, since the

models developed by CMDS consider the data of only one country, the results are

applicable only to that one country. The implication is that separate models, in terms of

multiple discriminant functions and binary logistic regressions must be considered in

Phase Four.

Variance explained and Stress by data set and dimensionality of solution are given

in Table 27. Scatterplots of these data are available in Charts 4 and 5, respectively. Once

again, for reasons of reproducibility the number of iterations used by the ALSCAL


algorithm for each country’s data set is available in Table 26. These solutions follow the

guidelines of a roughly inverse relationship between variance explained and stress.

Further, the rough inverse relationship between stress and dimensionality is also

maintained. Finally, the direct relationship between dimensionality and variance

explained is seen (that is, as the number of dimensions goes up so does the level of

variance explained). Note that not much improvement is demonstrated by moving from a

five to a six dimensional solution. However, for the purposes of verifying the Schwartz

model every available amount of variance explained was taken into consideration. As

such, the six dimensional solution was tested in Phase 4 of this study.

Phase Four

Tables 28 through 43 detail the development of the MDF models as they

differentiate between primary motivation types under the Schwartz model. The SPSS

syntax used to derive these solutions is available in Appendix N. Again, it must be

emphasized that it is unclear what relation the loadings of each MDS dimension (whether

this is a CMDS or WMDS solution) have with regard to each individual multiple

discriminant function (these loadings are shown in Tables 28, 32, 36, and 40). It is

notable, however, that the number of discriminant functions equals the number of WMDS

or CMDS dimensions. This not withstanding, in most cases no one dimension dominates

the contributions of variation to a discriminant function. The previous observation could

be taken to mean that no one dimension within the MDS models is responsible for the

differentiation of an overall motivation type or overall group of motivation types. This is

given further credence when one examines the centroids for each primary motivation type

(values for the centroids are available in Tables 30, 34, 38, and 42). That is, generally


speaking, no one primary motivation type comes out as more clearly classified at the

aggregate level than any other. A notable exception to this statement is that the primary

motivation type “Power” tends to have a high centroids value in the WMDS common

space, and CMDS United States and Romanian space models. Perhaps, this suggests that

this motivation type is clearly differentiated in these three models and stands out in terms

of aggregate distance when compared to the other motivation types.

Table 44 offers a summary of MDF results in the form of Press’s Q and Cohen’s

Kappa for common space developed through the WMDS algorithm and the individual

spaces developed for each CMDS algorithm by country. Notably, Press’s Q produced

extraordinarily high values. Once again, caution is recommended in the interpretation of

this statistic. One possible interpretation is that the assumptions of this statistic were

violated due to low cell counts. However, Cohen’s Kappa offers a more enlightening

look at these models viability. All of the models offer a classification rate near or above

50% even after controlling for chance. The beginnings of a trend are also notable in that

the common space model produced the highest classification rate while Romania offers

the lowest classification rate.

The observation that Romania fared the worst of the four models set forth is

apparent when one examines Tables 45 through 48. Remember when reading these

results that in the case of this study MDF and binary logistic regression are being used to

look into the MDS models that were generated in Phase 2. As such, the results of these

models are labeled the Observed results. The results that are predicted by Schwartz are

labeled Empirically Expected and are the analog to the predicted results.


Again, the common space model, whose results are illustrated in Table 45,

seemed to fair the best of the set with the United States coming in second. Of great

interest is the area where the primary motivation types Benevolence, Tradition,

Conformity, and Security intersect with regard to the expected and observed statistics.

This area shows a low level of differentiation with a large number of values falling

outside of the diagonal. This is not surprising when one considers that Tradition and

Conformity tend to occupy the same piece of the SSA pie in the Schwartz model (Figure

1).

The specifics of the binary logistic regression models (detailed in Tables 49

through 56) tend to repeat the story of the MDF analysis. The SPSS syntax used to

derive these models is available in Appendix O. Each predictor variable, in this case

each dimension, from each model produces its own function. However, in this case one

or two WMDS or CMDS dimensions dominate each function. This suggests that the

differentiation of Openness to Change versus Conservation and Self-Transcendence

versus Self-Enhancement relies almost entirely on these variables. However, this may

also have to do with the simplicity of the differentiation. That is, these are simplistic

choices between two classifications and unlikely to require input from the various

dimensions. Notably, the Romanian model with regard to differentiation between

Openness to Change versus Conservation, detailed in Table 52, failed to achieve

significance.

Cross tabulations showing the hits and misses of the logistic models are given in

Tables 57 through 64. These tend to reinforce the issue of the common space and United

States model tending to more closely mimic the Schwartz model.


Tables 65 and 66 are the logistic analogs to Table 44, for Openness to Change

versus Conservation and Self-Transcendence versus Self-Enhancement. The values

obtained for Press’s Q for all eight models are again extremely high (note that this

statement considers each individual space model, of which there are three, and each

common space model, for all of which there are two uses of binary logistic regression).

However, unlike the MDF results, the statistic maintains its interpretation. Again, the

trend mentioned with regard to the common space having the largest proportion of

correctly classified individual level values is maintained with the exception of Self-

Transcendence versus Self-Enhancement, in which the United States fairs the best.

Notably, the kappa values are lower for the dichotomous differentiations than for the

MDF differentiations. Further, Romania faired far worse than the United States and

Poland with regard to classification of values (Table 44, percentage agreement and

percentage agreement beyond chance).

Tables 67 through 70 give listing of each item of the SVS as classified by their

primary motivation types through MDF under the Schwartz model. Further, positions

under the semi-bipolar dimensions of Self-Transcendence versus Self-Enhancement and

Openness to Change versus Conservation, under the Schwartz model and as classified

through CMDS solutions by binary logistic regression. Table 71 give a listing of the

value statements that adhered to the expected motivation primary types and positions

under the semi-bidirectional dimensions. While Table 72 and Chart 6 show the value

statements that maintained the correct value primary motivation categories across all four

of the space developed through MDS by country.


A final result of keen interest is that Romania fared the worst of the three

countries studied. Overall, Romania showed the widest fluctuations with regard to

variance shared and stress in when the WMDS solution was attempted (Charts 2 and 3)

and the lowest number of correct classifications of values under the bipolar value

dimensions (Tables 57 and 64).

Further, evidence of this is suggested when the similarities between value

distances were measured within the CMDS models through multiple uses of Kendal’s

Tau-b coefficients between countries (Table 73, Pearson’s Correlation Coefficients are

provided in Table 74). Romania has the lowest level of association when compared to

the U.S. and Poland. This is demonstrated again in Table 75 where r is used. In this

table, Romania and the United States show the lowest level of similarity while the United

States and Poland are the most similar. Note that Table 76 offers the equivalent Kendal’s

Tau-b measures of association for comparability purposes between the concordance and

CMDS distance measures. This results summarized in this table tend to suggest the same

structure of relationships between the three countries.

Discussion

The results of this study support some, but not other, aspect of the Schwartz’s

model. Several reasons for the lack of strong support may exist. This could be due to a

number of factors such as the lack of stability regarding the overall architecture of the

SVS’s interrelationships across cultures, a bias in the SVS itself towards western values

or amalgam thereof, this study’s control of demographics, or a basic incompatibility of

this study’s analysis with SSA.


The results of this study suggest that the Schwartz model best fits an overall

amalgamation of United States, Poland, and Romania but has trouble when applied to

individual models. Unfortunately, the common space developed via ALSCAL’s WMDS

algorithm does not appear to be a good fit to the data. Specifically, it appears that there is

a large amount of instability in the common space solutions developed by this algorithm.

This would mean that each space developed through the CMDS process for the United

State, Poland, and Romania individually are very different in terms of their properties.

Credence is given to this observation when one considers that Tables 72 through 74 show

that the input data and corresponding CMDS distance solutions when compared between

countries are highly disparate.

This suggestion tends to be further supported by the results of the individual

CMDS solutions with regard to the classification of values into their 10 primary

motivation types via MDF. Further evidence is found in the two semi-bipolar value

dimensions (Openness to Change versus Conservation and Self-Enhancement versus

Self-Transcendence) for which Romania fared the worst and the U.S. common space

fared the best.

The pieces of evidence, disparity between the common space distances and

concordance measures, and differing results from the discriminant regressions and

logistic regressions strongly suggest that each common space is unique. First, the

common space distances and concordance measures show that the dissimilarities between

value statements when compared between countries are disparate. This means that each

matrix (whether in their original form of concordance measures or in their scaled form

via ALSCAL) simply are not comparable. This supported by the failure of ALSCAL to


render a satisfactory WMDS group space. Further, the patterns of values falling within

the primary motivations of Schwartz’s model are different. This means that each space is

conforming uniquely to the overarching theory Schwartz has set forth.

The implication that each of these common spaces is unique in structure when

compared to common spaces of other nations means that the interrelationships of the

SVS’s value statements are not universal. However, the results of the MDF and binary

logistic regression analyses (illustrated via hit rate tables) suggest that the overall

classification of values retains a very high degree of predictive validity. That is, the

higher level of abstraction offered by Schwartz (1992) in the form of Primary Motivators

and two semi-bipolar dimensions of Self-Enhancement versus Self-Transcendence and

Conservation versus Openness to Change may be universals of this model.

While these constraints are important, this does not diminish the utility of the

SVS. However, this implies that the SVS may not be a scale in the traditional sense.

Rather, in light of these results it appears that the SVS represents a taxonomy of value

types. That is, a scale implies that each value statement has a specific relationship with

other value statements, with each value statement definitively falling within each primary

motivation type. A taxonomy implies that the value statements tend to fall into a given

primary motivation type. The distinction of the term taxonomy as opposed to scale

centers on the idea that the primary motivation types are more categorical in nature as

opposed to a strict continuum as described under the original model.

The overall architecture of the SVS values may be more stable across cultures

with regard to certain value statements, however. Table 71 shows the value statements

that maintain their correct classification with regard to both primary motivations as


analyzed via MDF and the semi-bipolar dimensions as analyzed via binary logistic

regression. Only 19 of the 56 statements meet the criteria of being correctly classified in

the United States, Poland, and Romania. One of these value statements, item 43

“Capable,” was not correctly classified under the WMDS common space model. Note

that there appears to be no commonality to these value statements. Notably, there is no

apparent difference between whether these are end-states, such as “Sense of belonging,”

or instrumental values, such as “Creativity.” Further, the binary logistic analysis played

virtually no role in the correct classification of these values. That is, as long as any of the

56 values were classified correctly under the rubric of the ten primary motivation types,

as analyzed via MDF, they were highly unlikely to have been misclassified under the

rubrics of two semi-bipolar dimensions of Self-Enhancement versus Self-Transcendence

and Conservation versus Openness to Change, as analyzed via binary logistic regression.

Perhaps of equal concern to the issue of disparate value structures between

nations is that the CMDS United States model best supports the Schwartz model (The

common space derived from the WMDS algorithm is a better fit; however, this space has

previously been discussed and deemed a poor fit to the data). Two conclusions result

from this observation. First, it could be that the SVS was not translated properly. This

seems unlikely. The translation process was quite rigorous and the back translations

show a large amount of congruity to the original. The second option is that the Schwartz

model is biased towards American or Western European style values. That is, the better

performance of the United States could be an artifact of the scale’s original composition

in English.


Another possibility is that the statistical control of demographics may have altered

the value scores. This suggests the Schwartz model is heavily correlated with

demographics. Remember that the original studies by Schwartz used a highly

homogeneous population composed of students and teachers. This may have

“controlled” for the demographic influence. This hypothesis is very unfortunate as the

logical conclusion would be that value structure, again, is not fully universal.

Specifically, the issue would be that demographics within cultures unduly pollute this

structure. However, this leads to an even more complex problem: Are the demographics

effecting the values of those studied or are the values of those studied effecting their

demographics (that is, by impacting one’s readiness to seek higher education or to engage

in activities to enhance income)? This line of logic quickly becomes akin to the classic

nature versus nurture debate. Generally speaking, it is neither nature nor nurture; rather it

is the interaction of the two that make the individual, or in this case his or her values.

The next possibility is that in using listwise deletion with regard to both

demographics and the SVS this study selected for a specific class of respondents. Again,

this leads back to the question of whether the SVS is truly universal. That is, if the SVS

does truly tap the universal values structure guiding everyone then the use of respondents

who choose to fill out an entire questionnaire as opposed to those who do not should be

irrelevant.

Another possibility is that CMDS and WMDS combined with MDF and binary

logistic regression simply do not yield results comparable to SSA. This theory seems to

be unlikely in explaining the level of divergence from Schwartz’s model. However,

Todd & Lawson (2003) performed an MDS analysis of the SVS using PROXSCAL a


conceptual descendent of ALSCAL and confirmed the SVS’s structure through this

method. Unfortunately, this study used a shortened version of the SVS. Additionally,

this study did not use ALSCAL. Instead, PROXSCAL, a newer MDS algorithm, was

used in the production of this space. Further, Todd & Lawson (2003) chose to use

shortened scale8 and ipsatize9 the results by individual respondent. This would call into

question the compatibility of the current study’s results with that of Todd & Lawson’s

(2003).

The lack of universality between the value statements interrelationships also

means that countries may be classified by differential scores on SVS. That is the study

by Sverko (1995), mentioned in the introduction as using Hofstede’s Values Scale and

hierarchical clustering, could be repeated using the SVS. Specifically, this means that

cultures could be classified via this instrument.

Another research application of the previous discussion regarding SVS’s non-

universal structure is that this tends to open a window that would allow the researcher to

look into the impact of a culture’s values on various hypothetical constructs. That is,

these individual differences in scores, as represented in miss-classifications under the

various analysis models (MDF and binary logistic regression), could be correlated with

such scales as the Marlowe-Crowne Social Desirability Scale (Crowne & Marlowe, 1960)

or the Global Innovativeness Scale (Hurt, Joseph, & Cook, 1977). This could provide

new insights as the nature of these constructs as they vary by culture.

8 This scale was composed of seven instead of nine points. Negative one was anchored with opposition to the respondent’s values while the opposite end, five, was anchored as highly important to the respondent’s values. 9 In this case, the operational definition of “ipsatize” is to subtract the mean score of each respondent’s overall values score from each individual values statement rating. This procedure is often alternately termed, “centering.” Hence, if a respondent had a mean score of two for the overall questionnaire used and had rated the first item five; the “ipsatized” score would be three.


A final avenue of further research that was not pursued by this study would be to

examine the dimensions of the resultant CMDS spaces. Specifically, one could look for a

discernable pattern and use this pattern (if it exists) to reorganize the scale.

Unfortunately, this would most likely require the use of more than three cultures’ data if

one wished to propose an alternative to Schwartz’s taxonomical system. The three

cultures examined in this study give but an initial glimpse into the potential diversity

offered across the world.


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Figure 1 The Interrelationships of the Primary Motivation Types and Bipolar Value Dimensions for the Schwartz Social Values Scale

From: Schwartz, S. H. (1992). Universals in the content and structure of values theoretical advances and empirical tests in 20 countries. In M. P. Zanna (Ed.), Advances in Experimental Social Psychology: Vol. 25 (pp. 1-65). San Diego, CA: Academic Press.


Figure 2 Lin’s Concordance Coefficient (rlc)

rlc = 2( ab ) n

a2 + b2 + (MeanA MeanB)2 n n

Where

a = Each deviation score (Variable A score minus the mean for A)

b = Each deviation score (Variable B score minus the mean for B)

n = The number of units scored in common to both variables.


Figure 3 Linn’s Concordance Coefficient as an Excel Formula

rlc = ( 2 * COVAR ( A2 : A99 , B2 : B99 ) ) / ( VARP ( A2 : A99 ) + VARP ( B2 : B99 ) + ( AVERAGE( A2 : A99 ) - AVERAGE( B2 : B99 ) ) * ( AVERAGE ( A2 : A99 ) - AVERAGE ( B2 : B99 ) ) )

Where: The data set in question ranges from cell A2 to B99. The cell references A2 to A99 (A2:A99) specify the first subject’s data range. The cell references B2 toB99 (B2:B99) specify the second subject’s data range. AVERAGE ( ) = The arithmetic average function. COVAR ( ) = The covariance, the average of the products of deviations for each data point pair function. VARP ( ) = The variance for the entire population function.


Figure 4 Concordance to Distance Transformation

Distance = ( ABS | MA – 1 | )

Where: ABS | | = the absolute value for the value contained therein. MA = The measure of association, in this case Lin’s Concordance Coefficient (rlc)


Figure 5 Concordance to Distance Transformation as an Excel Formula

= ( ABS ( A1 - 1 ) )

Where: A1 is the cell of reference. ABS( ) = Returns the absolute value for the value contained therein.


Figure 6

The Conceptual Formula for Cohen’s Kappa ( )

= PAO - PAE

1 - PAE Where: PAO = Percentage Agreement between the Schwartz model and this study’s data PAE = (1 / n2)( pmi) n = Number of Units pmi = Product of each individual marginal


Figure 7 An Example Computation for Cohen’s Kappa ( ) Using the example data set from Table 8 and the marginal calculations from Table 9. PAO = 2 + 3 + 2 = 7 PAE = (1 / n2) ( pmi) = (1 / 102) (9 + 15 + 8) = (1 / 100) (32) = (.32)

Cohen’s kappa = PAO - PAE

1 - PAE

= .70 - .32 1 - .32

= .38 .68

= .56

From Neuendorf, K. A. (2002). The content analysis guidebook. Thousand Oaks, CA: Sage Publications.


Figure 8

Cohen’s Kappa ( ) as an Excel Formula

= ( ( ( SUM ( C60 , D61 , E62 , F63 , G64 , H65 , I66 , J67 , K68 , L69

) ) / 56 ) - ( ( 1 / ( 56 ^ 2 ) ) * ( ( C70 * M60 ) + ( D70 * M61 ) + ( E70 * M62 ) + ( F70 * M63 ) + ( G70 * M64 ) + ( H70 * M65 ) + ( I70 * M66 ) + ( J70 * M67) + ( K70 * M68 ) + ( L70 * M69 ) ) ) ) / ( 1 - * ( ( 1 / ( 56 ^ 2 ) ) * ( ( C70 * M60 ) + ( D70 * M61 ) + ( E70 * M62 ) + ( F70 * M63 ) + ( G70 * M64 ) + ( H70 * M65 ) + ( I70 * M66 ) + ( J70 * M67 ) + ( K70 * M68 ) + ( L70 * M69 ) ) ) )

Where: The cell range is a cross tabulation matrix with marginal sums ranging from C60 to M70 (including marginal sums).


Table 1

Values and Example Instrumental Iterations of the Rokeach Values Survey

Value Instrumental Iteration

Ability Utilization Use my skill and knowledge

Achievement Have results which show that I have done well

Advancement Get ahead

Aesthetics Make life more beautiful

Altruism Help people with problems

Authority Tell others what to do

Autonomy Act on my own

Creativity Discover, develop, or design new things

Economics Have a high standard of living

Life-Style Living according to my ideas

Personal Development Develop as a person

Physical Activity Get a lot of exercise

Prestige Be admired for my knowledge and skills

Risk Do risky things

Social Interaction Do things with other people

Social Relations Be with friends

Variety Have every day different some way from the one

before it.

Working Conditions Have good space and light in which to work



Table 2 Primary Motivations, Definitions of Primary motivations and Items that Compose the

Primary Motivations for the Schwartz Social Values Scale

Primary

Motivation

Definition Items That Compose the Primary

Motivations on the Schwartz Social

Values Scale

Power Social status and prestige,

control or dominance over

people and of resources.

Social Power, Authority, Wealth,

Preserving my Public Image.

Achievement Personal success through

demonstrating competence

according to social standards.

Successful, Capable, Ambitious,

Influential.

Hedonism Pleasure and sensuous

gratification for oneself.

Pleasure, Enjoying Life.

Stimulation Excitement, novelty, and

challenge in life.

Daring, A Varied Life, An Exciting

Life.

Self-Direction Independent thought and action

choosing, creating, exploring.

Creativity, Freedom, Independent,

Curious, Choosing own Goals.

Universalism Understand appreciation,

tolerance, and protection for the

welfare of all people and for

nature.

Broad-Minded, Wisdom, Social

Justice, Equality, A World at Peace,

A World of Beauty, Unity with

Nature, Protecting the Environment.

Benevolence Preservation and enhancement of

the welfare of people with whom

one is in frequent personal

contact.

Helpful, Honest, Forgiving, Loyal,

Responsible.

Tradition Respect, commitment, and

acceptance of the customs and

ideas that traditional culture or

Humble, Accepting my Portion in

Life, Devout, Respect for Tradition,

Moderate.


religion provides oneself.

Table 2 (Continued) Primary Motivations, Definitions of Primary Motivations and Items that Compose the

Primary Motivations for the Schwartz Social Values Scale

Primary

Motivation

Definition Items That Compose the

Primary Motivations on the

Schwartz Social Values Scale

Conformity Restraint of actions, inclinations, and

impulses likely to upset or harm others

and violate social expectations or

norms.

Politeness, Obedient, Self-

Discipline, Honoring Parents

and Elders.

Security Safety, harmony, and stability of

society, of relationships, and of self.

Family Security, National

Security, Social Order, Clean,

Reciprocation of Favors.



Table 3

Differential Value Ratings for Poland and the United States as measured by the Hofstede Values Survey

Nation Bipolar Value Index

Power

Distance

Individuality

versus

Collectivism

Masculinity

versus

Femininity

Uncertainty

Avoidance

Long Term

Orientation

Poland 55 60 65 78 37

United States 40 91 62 46 29

From: Braithwaite, V. & Scott, W. (1991). Values. In J. Robinson, P. Shaver, & L. Wrightsman (Eds.), Measures of Personality and Social Psychological Attitudes (pp. 651 – 753). San Diego, CA: Harcourt, Brace, Jovanovich Publisher.


Table 4 Differences in Vertical Versus Horizontal, Individualism and Collectivism between

Poland and the United States

Statistic

Mean Standard Deviation Alpha

Horizontal Collectivism

Poland 27.20 4.60 0.60

Untied States 25.19 4.50 0.64

Vertical Individualism

Poland 20.19 5.10 0.50

Untied States 18.26 5.60 0.68

Vertical Collectivism

Poland 40.05* 5.10 0.64

United States 37.80* 5.10 0.60

Horizontal Individualism

Poland 32.90 5.70 0.69

United States 33.75 5.70 0.77

*Significantly different as measured by ANOVA (p < 0.01).


Table 5 Mean cultural level values for Poland and the United States as measured by the Schwartz Social Values Scale

Nation Cultural Level Value

Conservatism Affective

Autonomy

Intellectual

Autonomy

Hierarchy Mastery Egalitarian

Commitment

Poland 4.31 3.13 4.09 2.53 4.00 4.82

United

States 3.90 3.65 4.20 2.39 4.34 5.03



Table 6 Hypothetical Variables for Association in Arbitrary Units

Observation

Variables

X Y Z

1

(N = 10)

1

(N = 10)

1

(N = 10)

3

2 2 2 4

3 3 3 5

4 4 4 6

5 5 5 7

6 6 6 8

7 7 7 9

8 8 8 10

9 9 9 11

10 10 10 12


Table 7 An Example Data Set for Cohen’s Kappa ( )

Coder B Coder A Total

1 2 3

1 2 1 0 3

2 0 3 0 3

3 1 1 2 4

Total 3 5 2 10



Table 8 Example Calculation of Marginal Products for the Example Data set Illustrating Cohen’s Kappa ( )

Category Marginals Products of Marginals

A x B Coder A Coder B

1 3 3 9

2 5 3 15

3 2 4 8

Total 10 10 -



Table 9 Means of Select Demographics for the Schwartz Social Values Survey (SVS) (After Listwise Deletion of Non-Responders to Demographics and the SVS)

Original

Question

Variable Dummy

Codes

Respondent Country

United States Poland Romania

Age

Age

(n = 98)

38.388*

(n = 201)

29.313*

(n = 128)

22.125*

Employment

Status

Self-Employed .082 .050 .039

Employed Full-

Time .571 .363 .164

Employed Part-

Time .184 .075 .031

Unemployed .020 .015 .047

Student .204 .458 .820

Home .051 .020 .008

Retired .020 .020 .008

Marital Status Married .633 .313 .047

Divorced Separated

or Widowed .061 .050 .023

Level of

Education

High School .061 .343 .430

Technical School /

Training .010 .035 .039

Some College /

University .245 .075 .367

College /

University .429 .303 .094

Graduate or

Professional

School

.255 .000 .055


Table 9 (Continued) Means of Select Demographics for the Schwartz Social Values Survey (SVS) (After Listwise Deletion of Non-Responders to Demographics and the SVS)

Original

Question

Variable Dummy

Codes

Respondent

Country

Original

Question

Variable

Dummy Codes

Occupation Professional .439 .239 .141

Managerial

Executive .122 .060 .070

Sales .041 .025 .094

Clerical .041 .139 .016

Labor with

Technical Training .020 .020 .031

Labor without

Technical Training .000 .015 .070

Gender Gender (Female = 1) .663 .667 .547

*Standard Deviations for the United States, Poland, and Romania, were 11.717, 14.296, and 3.299, respectively.


Table 10 Missing Values Summary by Country for Demographics

Variable Country

United

States

Poland Romania Total

Age

(n = 136)

3

(n = 266)

18

(n = 258)

17

(N = 660)

38

Level of Education 2 17 20 40

Household Size 1 16 29 46

Employment Status 0 17 22 39

Marital Status 1 17 16 34

Level of Education 2 17 20 39

Occupation* 0 17 21 38

Income Level 7 40 26 73

* The question regarding Occupation involved a skip pattern controlled by Employment Status.


Table 11 Demographics Common to All Three of the Surveys for the United States, Poland, and Romania

Question Data Level Number of

Categories

Entered into

Regression

Removed Level

Age Ratio Not

Applicable

Yes Not Applicable

Marital Status Categorical 3 to 4* Yes Single

Gender Categorical 2 Yes Male

Level of Education

Ordinal/Categorical** 6 Yes Some High School

Employment Status

Categorical 7 Yes Not Applicable***

Occupation**** Categorical 7 Yes Not Applicable*****

Income Level Ordinal/Categorical** 7 to 14 No Not Applicable Household Size Ratio Not

Applicable No Not Applicable

* The Marital Status question for the United States had four categories: Single, Married, Divorced/Separated, and Widowed. Poland and Romania had three categories: Single Married, and Divorced/Separated/Widowed. In order to standardize this question between countries the United States version of this question had its last two categories collapsed into one. ** Both Level of Education and Income Level could be interpreted as ordinal level variables. However, as regression does not make any differentiation between ordinal and categorical data levels, these two variables defaulted to the later data level. *** The question regarding employment status instructed respondents to check all categories that applied. While typically, when dummy coding categorical variables for entry into a parametric equation one deletes one category to avoid multi-colinearity, this condition allowed us to use all available levels from this question. **** Poland used a different skip pattern than the United States and Romania. For the former self-employment allowed the respondent to proceed to the Occupation question for the later two self-employment did not. As such, all data from Poland with regard to occupation for respondents who were self-employed was eliminated. While this did


result in the loss of data the issue of parallel or standardized levels of analysis between countries was deemed more important at this stage of the analysis. ***** Respondents were instructed to skip this question depending on their employment level. This alleviated the restriction of deleting one category for the purposes of entry to regression.


Table 12

Summary of Straight Entry Linear Regression for Selected Demographics Predicting

Schwartz Social Values Scale Item 7 “Sense of Belonging” United States Sample (n =

98)

Variable B Std. Error Beta T-Value Sig.

Model 1

(Constant) 4.243 .894 - 4.745 .000

Age -.011 .019 -.083 -.591 .556

Self Employment -.467 .721 -.081 -.648 .519

Full Employment .938 .554 .295 1.692 .095

Part-Time

Employment

-.087 .570 -.021 -.153 .879

Unemployed 2.266 1.345 .203 1.685 .096

Student .811 .533 .207 1.523 .132

Homemaker 1.369 .803 .191 1.705 .092

Retired .941 1.264 .084 .744 .459

Married .429 .506 .131 .847 .399

Divorced Separated

or Widowed

.223 .893 .034 .250 .803

High School -.586 .756 -.089 -.775 .441

Technical School or

Training

.104 1.669 .007 .062 .950

Some College /

University

-.387 .463 -.106 -.836 .406

Graduate or

Professional School

-.542 .423 -.150 -1.282 .204

Professional -.028 .415 -.009 -.068 .946

Managerial Executive .173 .596 .036 .290 .773


Sales .791 .878 .099 .901 .371

Clerical -.739 .910 -.093 -.812 .419

Table 12 (Continued)


Schwartz Social Values Scale Item 7 (Sense of Belonging) United States Sample (n =

98)


Model 1

(continued)

Labor with

Technical

Training

1.875 1.329 .168 1.412 .162

Gender 1.091 .379 .327 2.882 .005

Note: R2 = .248 (p < 0.226)


Table 13 Summary of Straight Entry Linear Regression for Selected Demographics Predicting Schwartz Social Values Scale Item 55 “Successful” United State Sample (n = 98)


1 Model (Constant) 4.450 .723 - 6.157 .000

Age .001 .015 .012 .087 .931

Self Employment .442 .582 .096 .758 .451

Full Employment .982 .448 .384 2.190 .032

Part-Time

Employment

.748 .461 .229 1.623 .109

Unemployed 1.580 1.087 .176 1.453 .150

Student 1.092 .431 .348 2.536 .013

Homemaker .233 .649 .041 .359 .720

Retired 1.362 1.022 .152 1.333 .187

Married -.510 .409 -.194 -1.248 .216

Divorced Separated

or Widowed

-.494 .722 -.094 -.684 .496

High School -.906 .611 -.172 -1.484 .142

Technical School or

Training

-1.553 1.349 -.123 -1.151 .253

Some College /

University

.172 .374 .058 .459 .648

Graduate or

Professional School

-.192 .342 -.066 -.561 .577

Professional .633 .335 .248 1.888 .063

Managerial Executive .980 .482 .254 2.035 .045

Sales 1.383 .710 .216 1.949 .055

Clerical .270 .736 .042 .368 .714


Table 13 (Continued) Summary of Straight Entry Linear Regression for Selected Demographics Predicting Schwartz Social Values Scale Item 55 “Successful” United State Sample (n = 98)


Model 1

(Continued)

Labor with

Technical

Training

.492 1.074 .055 .458 .648

Gender -.296 .306 -.110 -.967 .337

Note: R2 = .239 (p < 0.272)


Table 14


Schwartz Social Values Scale Item 1 “Equality” Polish Sample (n = 201)


Model 1 (Constant) 5.014 .499 - 10.054 .000

Age -.021 .019 -.163 -1.108 .270

Self-Employed -.867 .807 -.104 -1.074 .284

Employed Full Time .367 .751 .097 .489 .625

Employed Part Time .745 .765 .108 .974 .331

Unemployed -.321 1.093 -.021 -.293 .770

Homemaker .626 1.155 .048 .542 .588

Retired -1.048 1.075 -.081 -.975 .331

Married .429 .486 .110 .883 .379

Divorced Separated

or Widowed

1.092 .765 .131 1.428 .155

High school .601 .354 .157 1.701 .091

Technical School /

Training

-.380 .824 -.038 -.461 .645

Some College /

University

.805 .687 .116 1.172 .243

College / University 1.119 .555 .283 2.016 .045

Professional -.715 .470 -.168 -1.520 .130

Managerial Executive -1.240 .725 -.162 -1.710 .089

Sales -.276 .974 -.024 -.284 .777

Clerical -1.059 .591 -.202 -1.792 .075


Labor with Technical

Training

.582 1.077 .045 .541 .589

Labor without

Technical Training

-.857 1.069 -.057 -.802 .424



Schwartz Social Values Scale Item 1 “Equality” Polish Sample (n = 201)

Variable B Std. Error Beta t-value Sig.

Model 1

(Continued)

Gender .534 .298 .138 1.789 .075

Note: R2 = .123 (p < 0.207)


Table 15


Schwartz Social Values Scale Item 25 “A Varied Life” Polish Sample (n = 210)


Model 1 (Constant) 4.646 .402 - 11.559 .000

Age -.004 .015 -.035 -.234 .815

Self-Employed .208 .651 .031 .320 .749

Employed Full Time .522 .605 .174 .862 .390

Employed Part Time .630 .617 .115 1.021 .309

Unemployed .538 .881 .045 .611 .542

Homemaker .102 .931 .010 .110 .912

Retired -.823 .866 -.080 -.949 .344

Married -.509 .392 -.164 -1.300 .195

Divorced Separated

or Widowed

.596 .616 .090 .966 .335

High School .258 .285 .085 .905 .367

Technical School /

Training

1.007 .664 .128 1.516 .131

Some College /

University

.122 .553 .022 .220 .826

College / University -.107 .447 -.034 -.238 .812

Professional .166 .379 .049 .438 .662

Managerial Executive .412 .584 .068 .705 .482

Sales .381 .785 .041 .486 .628

Clerical -.392 .476 -.094 -.824 .411


Training

.583 .868 .057 .672 .502


Labor without

Technical Training

.815 .861 .069 .946 .345



Summary of Straight Entry Linear Regression for Selected Demographics Predicting Schwartz Social Values Scale Item 25 “A Varied Life” Polish Sample (n = 210)


Model 1

(Continued)

Gender .306 .241 .100 1.271 .205

Note: R2 = .091 (p < 0.585)


Table 16 Summary of Straight Entry Linear Regression for Selected Demographics Predicting Schwartz Social Values Scale Item 1 “Equality” Romanian Sample (n = 128)


Model 1 (Constant) 4.817 3.907 - 1.233 .220

Age .049 .126 .065 .390 .697

Self-Employed -1.997 1.603 -.155 -1.246 .216

Employed Full Time 1.408 1.497 .209 .940 .349

Employed Part Time 2.123 2.123 .148 1.000 .320

Unemployed -.232 1.256 -.020 -.185 .854

Student .665 1.309 .103 .508 .612

Retired 1.310 4.791 .046 .273 .785

Married -.922 1.225 -.078 -.753 .453

Divorced Separated or

Widowed

.522 1.660 .032 .314 .754

High school -.922 1.903 -.183 -.485 .629

Technical School /

Training

-3.200 2.250 -.249 -1.422 .158

Some College /

University

-1.072 1.904 -.208 -.563 .574

College / University -.877 2.131 -.103 -.411 .682

Graduate or

Professional School

-.868 2.273 -.079 -.382 .703

Professional -.120 .806 -.017 -.149 .882

Managerial Executive .816 1.077 .084 .758 .450

Sales -1.560 1.266 -.183 -1.232 .221

Clerical -1.939 1.864 -.097 -1.040 .301


Training

-1.121 1.323 -.078 -.847 .399


Table 16 (Continued) Summary of Straight Entry Linear Regression for Selected Demographics Predicting Schwartz Social Values Scale Item 1 “Equality” Romanian Sample (n = 128)


Model 1

(Continued)

Gender -.399 .976 -.041 -.409 .683

Labor without

Technical Training

-.576 .516 -.115 -1.116 .267

Note: R2 = .151 (p < 0.595)


Table 17


Schwartz Social Values Scale Item 26 (Wisdom) Romanian Sample (n = 128)


Model 1 (Constant) 4.912 2.938 - 1.672 .098

Age .075 .095 .133 .789 .432

Self-Employed .261 1.205 .028 .217 .829

Employed Full Time .765 1.126 .154 .679 .498

Employed Part Time .529 1.597 .050 .331 .741

Unemployed -.624 .944 -.072 -.660 .510

Student .035 .984 .007 .035 .972

Retired -4.857 3.603 -.232 -1.348 .181

Married -.904 .921 -.104 -.982 .329

Divorced Separated

or Widowed

.422 1.249 .035 .338 .736

High school -.695 1.431 -.187 -.486 .628

Technical School /

Training

-1.362 1.692 -.143 -.805 .423

Some College /

University

-.699 1.432 -.183 -.488 .627

College / University -1.335 1.603 -.211 -.833 .407

Graduate or

Professional School

-.478 1.710 -.059 -.279 .780

Professional -.133 .606 -.025 -.220 .827

Managerial Executive -.163 .810 -.023 -.201 .841

Sales -.679 .952 -.108 -.714 .477


Clerical -.793 1.402 -.053 -.566 .573



Summary of Straight Entry Linear Regression for Selected Demographics Predicting Schwartz Social Values Scale Item 26 (Wisdom) Romanian Sample (n = 128)


Model 1

(Continued)

Labor with

Technical

Training

.586 .995 .055 .589 .557

Labor

With Out

Technical

Training

.144 .734 .020 .196 .845

Gender -.636 .388 -.172 -1.638 .104

Note: R2 = .122 (p < 0.824)


Table 18 Degrees of Freedom Summary for Demographic Regression Models by Country

Source Country


Regression

(n = 98)

20

(n = 201)

20

(n = 128)

21

Residual 77 180 106

Total 97 200 127


Table 19 Independent Variable Collinearity Statistics for Hierarchical Regression by Country

Independent Variable Tolerance VIF

United

States

Poland Romania United

States

Poland Romania

Age .501 .224 .290 1.996 4.465 3.449

Self-Employed .623 .522 .515 1.605 1.914 1.943

Employed Full-Time .322 .123 .162 3.105 8.108 6.190

Employed Part-Time .498 .398 .364 2.009 2.515 2.750

Unemployed .671 .916 .705 1.491 1.092 1.419

Student .526 .618 .197 1.900 1.617 5.084

Home .777 .714 .279 1.287 1.401 3.585

Retired .759 .316 .741 1.317 3.160 1.350

Married .408 .582 .787 2.453 1.719 1.271

Divorced Separated or

Widowed

.528 .571 .056 1.892 1.752 17.870

High School .739 .705 .261 1.353 1.419 3.828

Technical School / Training .862 .494 .059 1.160 2.023 16.971

Some College .612 .247 .129 1.634 4.044 7.773

Graduate or Professional

School

.714 .400 .186 1.400 2.498 5.383

Professional .572 .545 .632 1.747 1.833 1.582

Managerial Executive .636 .700 .655 1.573 1.429 1.528

Sales .803 .384 .365 1.245 2.601 2.742

Clerical .748 .711 .929 1.337 1.406 1.076


Training

.687 .958 .936 1.455 1.044 1.068

Gender .757 .813 .797 1.321 1.230 1.255


Table 20 Univariate Descriptors of the 56 by 56 Matrix of Lin’s Concordance Coefficients for the Schwartz Social Values Scale

Measure Country


Mean 0.1991 0.3201 0.3773

Median 0.1951 0.3316 0.3811

Std. Deviation 0.1728 0.1309 0.1485

Variance 0.0299 0.0171 0.0221

Skewness 0.0692 -0.3609 -0.2172

Std. Error of Skewness 0.0624 0.0624 0.0624

Kurtosis -0.1255 -0.1657 0.1527

Std. Error of Kurtosis 0.1246 0.1246 0.1246

Range 1.1462 0.7548 1.0244

Minimum -0.4127 -0.0856 -0.2117

Maximum 0.7335 0.6691 0.8127

Percentiles 25 0.0811 0.2294 0.2899

50 0.1951 0.3316 0.3811

75 0.3162 0.4177 0.4744


Table 21 Variance Shared by Solution for Weighted Multidimensional Scaling Model by Country

Number of

Dimensions

Variance Shared

Average United States Poland Romania

2 0.409 0.344 0.571 0.312

3 0.530 0.362 0.621 0.607

4 0.594 0.53 0.648 0.604

5 0.564 0.636 0.714 0.343


Table 22 Stress by Solution for Weighted Multidimensional Scaling Model

Number of

Dimensions

Stress

Average United States Poland Romania

2 0.346 0.364 0.293 0.374

3 0.261 0.302 0.231 0.246

4 0.211 0.219 0.197 0.215

5 0.186 0.165 0.163 0.224


Table 23

Country Weight by Dimension and Dimensionality of Solution for the Weighted Multidimensional Scaling Models

Full

Dimensionality

of Solution

Country Country Weight by Dimension

1 2 3 4 5

2

United States .4617 .3617 - - -

Poland .5054 .5617 - - -

Romania .4313 .3543 - - -

3

United States .2271 .3379 .4430 - -

Poland .3350 .5411 .4652 - -

Romania .6548 .3176 .2782 - -

4

United States .3205 .2092 .3157 .5327 -

Poland .5367 .3145 .4742 .1912 -

Romania .3249 .6115 .3003 .1861 -

5

United States .3139 .2445 .2684 .5230 .3636

Poland .5815 .4705 .3035 .1754 .1771

Romania .2753 .2502 .3634 .1885 .1920


Table 24

Weirdness by Dimension and Dimensionality of Solution for the Weighted Multidimensional Scaling Model

Country Weirdness by Dimensionality of Solution

2 3 4 5

United States .0976 .2775 .4075 .3147

Poland .1239 .2034 .2788 .3061

Romania .0676 .3931 .3588 .2002


Table 25

Importance by Dimension and Dimensionality of Solution for the Weighted Multidimensional Scaling Model

Full

Dimensionality

of Solution

Importance of Each Dimension by Dimension Number

1 2 3 4 5

2 .2182 .1906 - - -

3 .1975 .1693 .1633 - -

4 .1654 .1722 .1382 .1183 -

5 .1708 .1146 .0987 .1133 .0668


Table 26 Iterations to Convergence for the Weighted Multidimensional Scaling Model and

Classic Multidimensional Scaling Models by Country Versus Number of Dimensions

Number of

Dimensions

Iterations to Convergence

Common

Space


1 - 5 6 4

2 6 5 5 7

3 7 6 6 5

4 23 9 6 7

5 32 6 8 6

6 - 10 7 6


Table 27 Stress and Variance Shared by Country Versus Number of Dimensions

Number of

Dimensions

Stress Variance Shared

United

States

Poland Romania United

States

Poland Romania

1 .45380 .41320 .38512 .42520 .56493 .61324

2 .27102 .24293 .26843 .62267 .74186 .69643

3 .19378 .18193 .19085 .71340 .79633 .78326

4 .14902 .14099 .15174 .77658 .84460 .82188

5 .11320 .11423 .11648 .83524 .87395 .86707

6 .09503 .09588 .09506 .86345 .89504 .89511


Table 28 Standardized Discriminant Function Loadings for the Weighted Multidimensional Scaling Model’s Common Space Primary Motivation Type classification

Variable Discriminant Function

1 2 3 4 5

Common Space Dimension 1 0.715 0.695 -0.046 -0.370 -0.014

Common Space Dimension 2 -0.577 0.716 -0.365 0.350 -0.076

Common Space Dimension 3 0.340 0.414 0.701 0.550 -0.036

Common Space Dimension 4 -0.559 0.214 0.392 -0.375 0.768

Common Space Dimension 5 0.877 -0.238 -0.453 0.471 0.404


Table 29 Correlations between Discriminant Functions and Dimensions for the Weighted Multi-Dimensional Scaling Model’s Common Space and the Schwartz Social Values Scale

Variable Function

1 2 3 4 5

Common Space Dimension 1 .472 .616* -.113 -.616 -.071

Common Space Dimension 2 -.380 .601* -.529 .460 .051

Common Space Dimension 3 .150 .211 .759* .596 -.045

Common Space Dimension 4 -.193 .135 .202 -.179 .934*

Common Space Dimension 5 .325 -.103 -.421 .459 .704*

* Largest absolute correlation between each variable and any discriminant function.


Table 30 Mean Scores for each primary Motivation Type (centroids) for the Discriminant Functions Analyzing the Weighted Multidimensional Scaling model of the Schwartz Social Values Scale

Primary Motivation Type

Common Space Model

Discriminant Function

1 2 3 4 5

Power 3.594 2.450 0.011 0.123 -0.015

Achievement 2.296 0.287 -0.507 -0.709 0.659

Hedonism 2.960 -1.822 1.044 -0.417 -0.421

Stimulation 2.507 -1.231 0.138 -1.609 -0.714

Self-Direction 1.013 -1.653 -0.294 0.549 0.011

Universalism -0.861 -1.520 -0.827 0.376 -0.008

Benevolence -2.608 -0.600 0.723 -0.523 0.110

Tradition -2.077 2.336 -0.887 -0.248 -0.577

Conformity -2.152 1.700 0.081 -0.244 0.411

Security -0.032 0.915 0.882 1.013 -0.041


Table 31 Common Space Tests of Significance for Multiple Discriminant Regression Analyzing the Weighted Multidimensional Scaling Model’s Common Space of the Schwartz Social Values Scale

Test of Function(s) Discriminant Functions

1 through 5 2 through 5 3 through 5 4 through 5 5

Wilks' Lambda 0.016 0.100 0.385 0.598 0.881

Chi-square 197.080 109.463 45.385 24.460 5.994

Degrees of Freedom 45 32 21 12 5

Significance 0.000 0.000 0.002 0.018 0.307

Canonical Correlation 0.918 0.861 0.597 0.568 0.344


Table 32 Standardized Discriminant Function Loadings for the Classic Multidimensional Scaling United States Model’s Primary Motivation Type Classifications


1 2 3 4 5 6

United States Space Dimension 1 0.774 -0.796 0.017 0.152 -0.335 0.423

United States Space Dimension 2 1.068 0.302 0.082 -0.190 0.130 -0.093

United States Space Dimension 3 -0.036 1.070 -0.164 0.080 -0.350 0.294

United States Space Dimension 4 -0.181 0.195 0.924 0.066 0.170 0.308

United States Space Dimension 5 0.323 0.139 0.135 0.882 -0.056 -0.380

United States Space Dimension 6 0.095 0.064 -0.468 0.374 0.683 0.447


Table 33 Correlations between Discriminant Functions and Dimensions for the Classic Multi-Dimensional Scaling Model for the United Space and the Schwartz Social Values Scale

Variable Function

1 2 3 4 5 6

United States Space Dimension 1 .698* .283 .133 -.422 .390 -.292

United States Space Dimension 2 -.069 .088 .853* .083 .257 .432

United States Space Dimension 3 .099 .055 .122 .861* -.098 -.471

United States Space Dimension 4 .024 .031 -.328 .301 .739* .505

United States Space Dimension 5 .001 .554 -.192 .131 -.600* .528

United States Space Dimension 6 .268 -.371 -.003 .211 -.554 .662*



Table 34 United States Mean scores (centroids) for each primary Motivation Type with Regard to Discriminant Functions for Classic Multidimensional Scaling Model of the Schwartz Social Values Scale


United States Model


1 2 3 4 5 6

Power -4.340 -0.331 1.040 0.071 -0.377 0.216

Achievement -1.311 -0.277 -1.313 1.342 0.261 0.016

Hedonism -1.201 1.896 -0.276 -1.309 0.035 -0.356

Stimulation -1.226 1.393 0.475 -0.083 0.621 -0.892

Self-Direction 0.169 1.784 -0.332 -0.325 0.035 0.168

Universalism 1.252 1.994 0.242 0.456 -0.196 0.039

Benevolence 2.174 -0.887 0.133 -0.310 0.012 0.199

Tradition 0.824 -1.546 1.380 0.388 0.517 -0.072

Conformity 1.160 -2.185 -0.325 0.178 -0.817 -0.488

Security -0.924 -1.631 -0.774 -0.666 0.190 0.099


Table 35 Tests of Significance for the Multiple Discriminant Regression Analyzing the Classic Multidimensional Scaling United States’ Space Model of the Schwartz Social Values Scale

Test of

Function(s)

Discriminant Functions

1 through 6 2 through 6 3 through 6 4 through 6 5 through 6 6

Wilks' Lambda 0.017 0.087 0.332 0.551 0.810 0.923

Chi-Square 190.170 115.001 51.828 28.038 9.910 3.749

Degrees of

Freedom 54 40 28 18 10 4

Significance 0.000 0.000 0.004 0.061 0.448 0.441

Canonical

Correlations 0.893 0.860 0.630 0.566 0.351 0.277


Table 36 Standardized Discriminant Function Loadings for the Classic Multidimensional Scaling Polish Space Model’s Common Space Primary Motivation Type Classification


1 2 3 4 5 6

Polish Space Dimension 1 0.698 0.772 -0.132 0.077 -0.106 -0.021

Polish Space Dimension 2 -0.838 0.614 -0.229 -0.036 0.048 0.090

Polish Space Dimension 3 -0.206 0.297 0.619 0.739 0.232 0.010

Polish Space Dimension 4 0.176 0.477 0.698 -0.520 0.300 0.226

Polish Space Dimension 5 0.220 -0.262 -0.089 0.181 -0.284 0.918

Polish Space Dimension 6 0.431 -0.210 -0.552 0.044 0.766 0.214


Table 37 Correlations between Discriminant Functions and Dimensions for Classic Multi-Dimensional Scaling Model Polish Space and the Schwartz Social Values Scale

Variable Function

1 2 3 4 5 6

Polish Space Dimension 1 -.639* .539 -.423 -.082 .119 .318

Polish Space Dimension 2 .544 .688* -.285 .212 -.307 -.098

Polish Space Dimension 3 -.065 .094 .431 .836* .320 .018

Polish Space Dimension 4 .055 .169 .561* -.642 .365 .329

Polish Space Dimension 5 .126 -.058 -.372 .085 .897* .178

Polish Space Dimension 6 .033 -.066 -.022 .142 -.321 .933*



Table 38 Polish Mean scores (centroids) for each Primary Motivation Type for Discriminant Functions Analyzing the Classic Multidimensional Scaling (WMDS) Model of the Schwartz Social Values Scale (SVS)


Polish Model


1 2 3 4 5 6

Power -1.581 -3.206 0.304 -0.707 0.367 0.088

Achievement -2.124 -0.954 0.273 0.510 -0.266 -0.306 Hedonism -4.147 1.183 0.271 -0.330 -0.762 0.213 Stimulation -3.214 0.943 1.271 0.860 1.092 0.097 Self-Direction -0.674 0.910 -1.044 0.381 -0.039 0.124 Universalism 0.740 1.729 0.351 -0.272 0.162 -0.240 Benevolence 1.105 1.527 0.388 -0.246 -0.002 0.170 Tradition 2.184 -1.750 1.328 0.165 -0.418 -0.022 Conformity 2.444 -1.431 -0.050 0.644 0.083 0.196 Security 0.688 -0.834 -1.718 -0.166 0.028 -0.093


Table 39 Tests of Significance for Multiple Discriminant Regression of the Classic Multidimensional Scaling Polish Space Model Analyzing the Schwartz Social Values Scale

Test of

Function(s)



Wilks' Lambda 0.018 0.087 0.357 0.698 0.847 0.963

Chi-Square 189.320 115.032 48.370 16.895 7.806 1.769 Degrees of

Freedom 54 40 28 18 10 4

Significance 0.000 0.000 0.010 0.530 0.648 0.778 Canonical

Correlations 0.891 0.871 0.699 0.419 0.347 0.192


Table 40 Standardized Discriminant Function Loadings for the Classic Multidimensional Scaling Analysis of the Romanian Space Model’s Common Space Classifying the Primary Motivation Types


1 2 3 4 5 6

Romanian Space Dimension 1 -0.923 0.321 0.240 0.491 -0.043 0.257

Romanian Space Dimension 2 -0.111 -0.888 0.303 0.209 0.280 0.144

Romanian Space Dimension 3 1.039 0.175 0.295 0.259 -0.139 0.255

Romanian Space Dimension 4 0.218 0.005 -0.806 0.253 0.275 0.443

Romanian Space Dimension 5 0.132 0.480 0.300 -0.081 0.834 -0.055

Romanian Space Dimension 6 -0.238 0.004 0.204 -0.639 -0.080 0.717


Table 41 Correlations between Discriminant Functions and Dimensions for Classic Multi-Dimensional Scaling Model Romanian Space and the Schwartz Social Values Scale

Variable Function

1 2 3 4 5 6

Romanian Space Dimension 1 .551* .173 .440 .457 -.272 .434

Romanian Space Dimension 2 -.017 -.799* .314 .271 .389 .193 Romanian Space Dimension 3 .068 -.016 -.748* .265 .327 .508 Romanian Space Dimension 4 -.417 .329 .273 .691* -.130 .384 Romanian Space Dimension 5 .046 .321 .244 -.127 .902* -.069 Romanian Space Dimension 6 -.081 .015 .163 -.639 -.068 .744*



Table 42 Romanian Mean Scores (centroids) for each Primary Motivation Type with regard to Discriminant Functions for Weighted Multidimensional Scaling Model of the Schwartz Social Values Scale


Romanian Model


1 2 3 4 5 6

Power -3.477 -1.059 0.349 0.152 -0.083 0.140

Achievement -0.915 0.335 -0.791 0.112 -0.194 -0.105

Hedonism -1.903 0.478 0.184 -0.198 -0.354 -0.430

Stimulation -2.342 0.318 0.061 -0.949 0.550 0.128

Self-Direction -0.359 0.663 -0.644 0.330 0.367 0.031

Universalism 0.927 0.148 0.414 -0.363 0.167 -0.065

Benevolence 0.903 0.333 -0.181 -0.234 -0.349 0.183

Tradition 1.731 -1.789 -0.451 -0.044 0.110 -0.094

Conformity 1.318 -0.155 0.044 0.327 -0.100 0.004

Security 0.638 0.306 0.861 0.449 0.039 0.005


Table 43 Tests of Significance for Multiple discriminant Regression of the Classic Multidimensional Scaling Romanian Space Model of the Schwartz Social Values Scale

Test of

Function(s)



Wilks' Lambda 0.099 0.373 0.601 0.795 0.905 0.976

Chi-square 108.647 46.327 23.923 10.809 4.693 1.141 Degrees of

Freedom 54 40 28 18 10 4

Significance 0.000 0.228 0.686 0.902 0.911 0.888 Canonical

Correlations 0.857 0.616 0.493 0.349 0.270 0.155


Table 44 Multiple Discriminant Regression Analyses Overall Significance of and Percentage Agreement Beyond Chance for the Schwartz Social Values Scale’s Ten Primary Motivation Types

Country Statistic

Press’s Q -

Significance of Discriminant

Modela

(N = 56)

Cohen’s Kappa ( ) -

Percent Agreement

Beyond Chance

(N = 56)

Common Space 323.8** 79.8%

United States 248.6** 70.0%

Poland 195.6** 61.6%

Romania 128.0** 49.9%

**p < 0.001 a Note that Press’ Q is based on Chi- Squared ( 2). Within the matrix generated by this analysis there were more than five cells with a count of zero. This calls into question the validity of this statistic for this application.


Table 45 Observed versus Empirically Expected Categorization of Values by Primary Motivation for Common Space of the Weighted Multidimensional Scaling Analyzed by Multiple Discriminant Regression

Observed Primary

Motivation Type

Primary Motivation Type as Empirically Expected

Total 1 2 3 4 5 6 7 8 9 10

1. Power 5 1 0 0 0 0 0 0 0 0 6

2. Achievement 0 3 0 0 0 0 0 0 0 1 4

3. Hedonism 0 0 3 1 0 0 0 0 0 0 4

4. Stimulation 0 0 0 1 1 0 0 0 0 0 2

5. Self-

Direction 0 1 0 0 6 0 0 0 0 0

7

6. Universalism 0 0 0 0 0 8 0 0 0 0 8

7. Benevolence 0 0 0 0 0 1 8 0 0 0 9

8. Tradition 0 0 0 0 0 0 0 3 1 0 4

9. Conformity 0 0 0 0 0 0 0 2 3 0 5

10. Security 0 0 0 0 0 0 1 0 0 6 7

Total 5 5 3 2 7 9 9 5 4 7 56

Table 46 Observed Versus Empirically Expected Categorization of Values by Primary Motivation for the United States’ Space Classic Multidimensional Scaling Model Analyzed By Multiple Discriminant Regression

Observed Primary

Motivation Type


Total 1 2 3 4 5 6 7 8 9 10

1. Power 5 0 0 0 0 0 0 0 0 0 5

2. Achievement 0 4 0 0 1 0 0 0 0 1 6


3. Hedonism 0 0 2 1 1 0 0 0 0 0 4

4. Stimulation 0 0 1 1 0 0 0 1 0 0 3

5. Self-Direction 0 0 0 0 4 1 0 0 0 0 5

6. Universalism 0 0 0 0 1 8 0 0 0 0 9

7. Benevolence 0 0 0 0 0 0 5 0 0 0 5

8. Tradition 0 0 0 0 0 0 1 3 1 0 5

9. Conformity 0 0 0 0 0 0 3 0 3 0 6

10. Security 0 1 0 0 0 0 0 1 0 6 8

Total 5 5 3 2 7 9 9 5 4 7 56


Table 47 Observed versus Empirically Expected Categorization of Values by Primary Motivation

for the Polish Space Classic Multidimensional Scaling Model Analyzed by Multiple

Discriminant Regression

Observed Primary

Motivation Type


1 2 3 4 5 6 7 8 9

1. Power 4 2 0 0 0 0 0 0 0

2. Achievement 0 2 0 0 0 0 0 0 0

3. Hedonism 0 0 3 0 0 0 0 0 0

4. Stimulation 0 0 0 2 0 0 0 0 0

5. Self-Direction 0 1 0 0 5 1 1 0 0

6. Universalism 0 0 0 0 1 5 3 0 0

7. Benevolence 0 0 0 0 0 2 4 0 0

8. Tradition 0 0 0 0 0 0 1 3 1

9. Conformity 0 0 0 0 0 0 0 1 3

10. Security 1 0 0 0 1 1 0 1 0

Total 5 5 3 2 7 9 9 5 4


Table 48 Observed Versus Empirically Expected Categorization of Values by Primary Motivation for the Romanian Space Classic Multidimensional Scaling Model Analyzed my Multiple Discriminant Regression

Observed Primary

Motivation Type


Total 1 2 3 4 5 6 7 8 9 10

1. Power 3 1 0 0 0 0 0 0 0 0 4

2. Achievement 1 3 1 0 1 0 0 0 0 0 6

3. Hedonism 1 0 2 1 0 0 1 0 0 0 5

4. Stimulation 0 0 0 1 0 1 0 0 0 0 2

5. Self-

Direction

0 1 0 0 4 0 0 0 1 3 9

6. Universalism 0 0 0 0 0 5 1 0 0 0 6

7. Benevolence 0 0 0 0 1 1 4 0 0 0 6

8. Tradition 0 0 0 0 0 1 0 4 1 0 6

9. Conformity 0 0 0 0 0 0 2 1 1 0 4

10. Security 0 0 0 0 1 1 1 0 1 4 8

Total 5 5 3 2 7 9 9 5 4 7 56


Table 49

Logistic Regression Classification Results for Openness to Change Versus Conservation

of the Schwartz Social Values Scale Common Space Model generated by Weighted

Multidimensional Scaling Model’s Common Space

Independent Variable B S.E. Wald Sig. Exp (B)

Common Space Dimensions 1 .522 .498 1.097 .295 1.685

Common Space Dimensions 2 3.924 1.436 7.471 .006 50.614


Common Space Dimensions 4 2.356 .952 6.125 .013 10.546

Common Space Dimensions 5 -2.787 .960 8.432 .004 .062

Constant .630 .635 .984 .321 1.877

-2 Log Likelihood 20.039

Chi Square 57.308 df = 5

p = 0.001

Cox & Snell r2 0.641

Nagelkerke r2 0.856


Table 50

Logistic Regression Classification Results for Openness to Change Versus

Conservation of the Schwartz Social Values Scale’s Individual Space as Generated by

Classic Multidimensional Scaling Model for the United States

Independent Variables B S.E. Wald Sig. Exp (B)

United States Space Dimension 1 2.132 .696 9.374 .002 8.435

United States Space Dimension 2 -.869 .556 2.447 .118 .419

United States Space Dimension 3 -3.620 1.152 9.880 .002 .027

United States Space Dimension 4 .316 .523 .365 .546 1.372

United States Space Dimension 5 -1.447 .685 4.457 .035 .235

United States Space Dimension 6 -1.255 .772 2.646 .104 .285

Constant .660 .575 1.317 .251 1.934



p = 0.001


Nagelkerke r2 0.828


Table 51 Logistic Regression Classification Results for Openness to Change Versus Conservation of the Schwartz Social Values Scale in the Common Space Generated by Classic Multidimensional Scaling for Poland

Independent Variables B S.E. Wald Sig. Exp(B)

Polish Space Dimension 1 -.001 .258 .000 .998 .999

Polish Space Dimension 2 -1.987 .573 12.022 .001 .137

Polish Space Dimension 3 -.792 .455 3.036 .081 .453


Polish Space Dimension 5 .833 .518 2.580 .108 2.300

Polish Space Dimension 6 .934 .598 2.444 .118 2.545

Constant .217 .397 .299 .585 1.242



p = 0.001


Nagelkerke r2 0.611


Table 52 Logistic Regression Classification Results for Openness to Change Versus Conservation of the Schwartz Social Values Scale for the Common Space Generated by Classic Multidimensional Scaling for Romania


Romanian Space Dimension 1 -.150 .221 .464 .496 .860

Romanian Space Dimension 2 .665 .304 4.807 .028 1.945



Romanian Space Dimension 5 -.592 .387 2.336 .126 .553


Constant .210 .300 .490 .484 1.234



p = 0.085


Nagelkerke r2 0.240


Table 53 Logistic Regression Classification Results for Self-Enhancement Versus Self-Transcendence of the Schwartz Social Values Scale for the Common Space Generated by Weighted Multidimensional Scaling


Common Space Dimensions 1 -7.714 3.249 5.636 .018 .000


Common Space Dimensions 3 -4.864 2.113 5.301 .021 .008

Common Space Dimensions 4 .309 .732 .178 .673 1.362

Common Space Dimensions 5 -1.924 .991 3.769 .052 .146

Constant 1.586 .934 2.881 .090 4.883



p < 0.001


Nagelkerke r2 0.871


Table 54 Logistic Regression Classification Results for Self-Enhancement Versus Self-Transcendence of the Schwartz Social Values Scale as Generated by Classic Multidimensional Scaling for the United States



United States Space Dimension 2 4.373 1.473 8.808 .003 79.282





Constant 2.001 .923 4.701 .030 7.398



p < 0.001


Nagelkerke r2 0.843


Table 55 Logistic Regression Classification Results for openness Self-Enhancement Versus Self-Transcendence of the Schwartz Social Values Scale (SVS) in common space as generated by Classic Multidimensional Scaling (CMDS) model’s Polish space


Polish Space Dimension 1 4.003 1.384 8.369 .004 54.781

Polish Space Dimension 2 -1.115 .533 4.370 .037 .328

Polish Space Dimension 3 1.415 .756 3.504 .061 4.116

Polish Space Dimension 4 2.217 1.054 4.421 .035 9.181

Polish Space Dimension 5 .544 .744 .535 .465 1.723


Constant 1.365 .801 2.900 .089 3.914



p < 0.001


Nagelkerke r2 0.794


Table 56 Logistic regression classification results for openness to change versus Conservation of the Schwartz Social Values Scale (SVS) in common space as generated by Classic Multidimensional Scaling (CMDS) model’s Romanian space


Romanian Space Dimension 1 -1.478 .509 8.434 .004 .228

Romanian Space Dimension 2 -.610 .442 1.904 .168 .543

Romanian Space Dimension 3 1.485 .535 7.689 .006 4.413


Romanian Space Dimension 5 .199 .435 .208 .648 1.220


Constant .373 .377 .982 .322 1.452



p < 0.001


Nagelkerke r2 0.526


Table 57 A Cross tabulation of the Logistic Regression Results for the Classification of Values by Openness to Change and Conservation in Common space

Empirically Expected Observed Total

Openness to Change Conservation

Openness to Change 24 2 26

Conservation 2 28 30

Total 26 30 56


Table 58 A Cross tabulation of the Logistic Regression Results for the Classification of Values by Openness to Change and Conservation in United States space





Total 25 31 56


Table 59 A Cross Tabulation of the Logistic Regression Results for the Classification of Values by Openness to Change and Conservation in Polish space





Total 23 33 56


Table 60 A Cross Tabulation of the Logistic Regression Results for the Classification of Values by Openness to Change and Conservation in Romanian Space





Total 25 31 56


Table 61 A Cross Tabulation of the Logistic Regression Results for the Classification of Values by Self-Enhancement and Self-Transcendence in Common space

Empirically

Expected

Observed Total

Self-Enhancement Self-Transcendence

Self-Enhancement 19 3 22

Self-Transcendence 2 32 34 Total 21 35 56


Table 62 A Cross tabulation of the Logistic Regression Results for the Classification of Values by Self-Enhancement and Self-Transcendence in United States Space

Empirically

Expected

Observed Total



Self-Transcendence 2 32 34 Total 22 34 56


Table 63 Cross Tabulation of the Logistic Regression Results for the Classification of Values by Self-Enhancement and Self-Transcendence in Polish Space

Empirically

Expected

Observed Total



Self-Transcendence 3 31 34

Total 21 35 56


Table 64 A Cross Tabulation of the Logistic Regression Results for the Classification of Values by Self-Enhancement and Self-Transcendence in Romanian Space

Empirically

Expected

Observed Total



Self-Transcendence 5 29 34

Total 18 38 56


Table 65 Logistic Regression Models’ Significance and Percentage Agreement Beyond Chance for the Schwartz Social Values Scale when Classifying between Openness to Change and Conservation by Multidimensional Scaling Model

Country Statistic

Press’s Q Value -

Significance of

Discriminant

Model

(N = 56)

Percentage

Agreement

(N = 56)


Percent Agreement

Beyond Chance

(N = 56)

Common Space 3,844.6** 42.9% 85.6%

United States 3,680.6** 41.1% 82.0%

Poland 2,772.1** 33.9% 60.2%

Romania 1,992.1** 30.4% 38.8%

**p < 0.001


Table 66 Logistic Regression Models’ Significance and Percentage Agreement Beyond Chance for the Schwartz Social Values Scale When Classifying Between Self-Enhancement and Self-Transcendence by Multidimensional Scaling Model

Country Statistic

Press’s Q Value -

Significance of

Discriminant

Model

(N = 56)

Percentage

Agreement

(N = 56)


Percent Agreement

Beyond Chance

(N = 56)

Common Space 3,680.6** 33.9% 81.1%

United States 3,844.6** 35.7% 85.0%

Poland 3,363.5** 32.1% 73.6%

Romania 2,366.0** 23.2% 45.9%

**p < 0.001


Table 67 Empirically Expected Versus Observed Classification of Primary Motivation Type and

Bipolar Value Dimensions of the Schwartz Social Values Scale for Multiple

Discriminant Functions and Logistic Regression Analysis in Common Space

Item

Number

Item Label Empirically Expected

Motivation Type

Observed

Motivation Type

1 Equality Universalism Universalism

2 Inner Universalism Universalism

3 Social Power Power

4 Pleasure Hedonism Hedonism

5 Freedom Self-Direction Self-Directions

6 Spiritual Benevolence Benevolence

7 Sense Security Security

8 Social Security Security

9 Exciting Stimulation Hedonism

10 Meaning Benevolence Security

11 Politeness Conformity Conformity

12 Wealth Power Power

13 National Security Security

14 Self-Respect Self-Direction Self-Direction

15 Reciprocation Security Security

16 Creativity Self-Direction Stimulation

17 World Universalism Universalismo

18 Respect Tradition Conformitys

19 Mature Benevolence Benevolence

20 Self-Discipline Conformity Tradition

21 Privacy Self-Direction Self-Direction

22 Family Security Securitys

23 Social Power Powero


24 Unity With Nature Universalism Universalism


Table 67 (Continued) Empirically Expected Versus Observed Classification of Primary Motivation Type and



Item Number Item Label Empirically Expected

Motivation Type

Observed Motivation

Type

25 Varied Life Hedonism Hedonism

26 Wisdom Universalism Benevolence

27 Authority Power Power

28 True Love Benevolence Benevolence

29 World Universalism Universalism

30 Social Universalism Universalism

31 Independent Self-Direction Self-Direction

32 Moderate Tradition Tradition

33 Loyal Benevolence Benevolenceo

34 Ambitious Achievement Achievement

35 Broad-minded Universalism Universalism

36 Humble Tradition Tradition

37 Daring Stimulation Stimulation

38 Protecting Universalism Universalism

39 Influential Achievement Powero

40 Honoring Conformity Conformity

41 Choosing Self-Direction Self-Direction

42 Healthy Security Securitys

43 Capable Achievement Self-Directions

44 Accepting Tradition Tradition

45 Honest Benevolence Benevolence

46 Preserving Power Power

47 Obedient Conformity Conformity


48 Intelligent Achievement Achievement

49 Helpful Benevolence Benevolence






Motivation Type

Observed Motivation

Type

50 Enjoying Hedonism Hedonism

51 Devout Tradition Conformity

52 Responsible Benevolence Benevolence

53 Curious Self-Direction Self-Direction

54 Forgiving Benevolence Benevolence

55 Successful Achievement Achievement

56 Clean Security Achievement

s Indicates that the item was inappropriately classified with regard to self-enhancement versus self-transcendence classification when Schwartz’s prediction was checked via logistic regression. o Indicates that the item was inappropriately classified with regard to openness to change versus Conservation classification when Schwartz’s prediction was checked via logistic regression.




Discriminant Functions and Logistic Regression Analysis in the United States’ Space

Item

Number


Motivation Type

Observed Motivation

Type

1 Equality Universalism Universalism

2 Inner Harmony Universalism Self-Direction

3 Social Power Power Power


5 Freedom Self-Direction Hedonismo

6 Spiritual Life Benevolence Benevolence

7 Sense of Belonging Security Security

8 Social Order Security Security

9 Exciting Life Stimulation Hedonism

10 Meaning in Life Benevolence Tradition


12 Wealth Power Powero

13 National Security Security Security

14 Self-Respect Self-Direction Self-Direction

15 Reciprocation of Favors Security Security

16 Creativity Self-Direction Self-Direction

17 World at Peace Universalism Universalism

18 Respect for Tradition Tradition Security

19 Mature Love Benevolence Benevolence

20 Self-Discipline Conformity Tradition


22 Family Security Security Securitys

23 Social Recognition Power Power


24 Unity with Nature Universalism Universalism

25 Varied Life Hedonism Stimulation




Discriminant Functions and Logistic Regression Analysis in the United States’ Space

Item

Number


Motivation Type

Observed Motivation

Type

26 Wisdom Universalism Universalism


28 True Friendship Benevolence Benevolence

29 World of Beauty Universalism Universalism

30 Social Justice Universalism Universalism

31 Independent Self-Direction Self-Directions

32 Moderate Tradition Stimulation

33 Loyal Benevolence Conformity

34 Ambitious Achievement Achievemento



37 Daring Stimulation Stimulations

38 Protecting the Environment Universalism Universalism

39 Influential Achievement Securityo

40 Honoring of Parent Conformity Conformity

41 Choosing own Goals Self-Direction Achievements

42 Healthy Security Securityo

43 Capable Achievement Achievement

44 Accepting my Portion in Life Tradition Tradition

45 Honest Benevolence Conformity

46 Preserving my Public Image Power Power


Table 68 (Continued) Empirically Expected versus Observed Classification of Primary Motivation Type for United States Discriminant Regression and Classic Multidimensional Scaling (CMDS) Model

Item

Number


Motivation Type

Observed Motivation

Type

47 Obedient Conformity Conformity

48 Intelligent Achievement Achievement


50 Enjoying Life Hedonism Hedonism

51 Devout Tradition Tradition

52 Responsible Benevolence Conformity

53 Curious Self-Direction Universalism


55 Successful Achievement Achievement

56 Clean Security Achievement





Discriminant Functions and Logistic Regression Analysis in Polish Space

Item

Number


Motivation Type

Observed Motivation

Type

1 Equality Universalism Benevolence

2 Inner Harmony Universalism Benevolenceo



5 Freedom Self-Direction Self-Direction

6 Spiritual Life Benevolence Benevolence


8 Social Order Security Securitys

9 Exciting Life Stimulation Stimulation

10 Meaning in Life Benevolence Benevolence


12 Wealth Power Power

13 National Security Security Security

14 Self-Respect Self-Direction Self-Directionso

15 Reciprocation of Favors Security Security

16 Creativity Self-Direction Self-Directions

17 World at Peace Universalism Securityo

18 Respect for Tradition Tradition Security

19 Mature Love Benevolence Universalismo

20 Self-Discipline Conformity Conformity

21 Privacy Self-Direction Securityso

22 Family Security Security Self-Directions


23 Social Recognition Power Security

24 Unity with Nature Universalism Universalism




Item

Number


Motivation Type

Observed

Motivation Type


26 Wisdom Universalism Self-Direction


28 True Friendship Benevolence Universalismo

29 World of Beauty Universalism Universalism

30 Social Justice Universalism Universalismo



33 Loyal Benevolence Universalismo

34 Ambitious Achievement Achievement


36 Humble Tradition Conformity

37 Daring Stimulation Stimulation

38 Protecting the Environment Universalism Universalismo

39 Influential Achievement Power

40 Honoring of Parent Conformity Conformity

41 Choosing own Goals Self-Direction Universalism

42 Healthy Security Securitys

43 Capable Achievement Achievement

44 Accepting my Portion in Tradition Tradition


Life


46 Preserving my Public Image Power Power





Item

Number


Motivation Type

Observed

Motivation Type

47 Obedient Conformity Tradition

48 Intelligent Achievement Self-Directions

49 Helpful Benevolence Benevolenceo

50 Enjoying Life Hedonism Hedonism


52 Responsible Benevolence Self-Direction

53 Curious Self-Direction Self-Direction

54 Forgiving Benevolence Tradition

55 Successful Achievement Powero

56 Clean Security Security





Discriminant Functions and Logistic Regression Analysis in Romanian Space

Item

Number


Motivation Type

Observed

Motivation Type

1 Equality Universalism Securityo

2 Inner Harmony Universalism Benevolenceo



5 Freedom Self-Direction Self-Directions

6 Spiritual Life Benevolence Conformity


8 Social Order Security Security

9 Exciting Life Stimulation Hedonism

10 Meaning in Life Benevolence Securitys

11 Politeness Conformity Security

12 Wealth Power Powero

13 National Security Security Securitys

14 Self-Respect Self-Direction Security

15 Reciprocation of Favors Security Securitys

16 Creativity Self-Direction Self-Direction

17 World at Peace Universalism Traditiono

18 Respect for Tradition Tradition Conformity

19 Mature Love Benevolence Conformity

20 Self-Discipline Conformity Conformity


22 Family Security Security Self-Directionso


23 Social Recognition Power Hedonismo

24 Unity with Nature Universalism Universalismo




Item

Number


Motivation Type

Observed Motivation

Type


26 Wisdom Universalism Universalism


28 True Friendship Benevolence Hedonismso

29 World of Beauty Universalism Universalismo

30 Social Justice Universalism Universalism



33 Loyal Benevolence Universalismo

34 Ambitious Achievement Self-Directions

35 Broad-minded Universalism Stimulations


37 Daring Stimulation Stimulations

38 Protecting the

Environment

Universalism Universalismo

39 Influential Achievement Powero

40 Honoring of Parent Conformity Self-Direction

41 Choosing own Goals Self-Direction Achievements

42 Healthy Security Self-Directionso


43 Capable Achievement Achievemento

44 Accepting my Portion in

Life

Tradition Tradition






Item

Number


Motivation Type

Observed Motivation

Type

46 Preserving my Public

Image

Power Achievement

47 Obedient Conformity Tradition

48 Intelligent Achievement Achievements


50 Enjoying Life Hedonism Achievement


52 Responsible Benevolence Benevolenceo

53 Curious Self-Direction Benevolenceo


55 Successful Achievement Achievements

56 Clean Security Self-Directionso



Table 71 Value Statements from Schwartz Social Values Scale that maintain the Same Primary

Motivation Type between the United States, Poland, Romania, and Common Space

Multidimensional Scaling solutions


Motivation Type

3 Social Power Power

4 Pleasure Hedonism

7 Sense of Belonging Security

8 Social Order Security

12 Wealth Power

13 National Security Security

15 Reciprocation of Favors Security

16 Creativity Self-Direction

24 Unity with Nature Universalism

27 Authority Power

29 World of Beauty Universalism

30 Social Justice Universalism

31 Independent Self-Direction

37 Daring Stimulation

38 Protecting the Environment Universalism

43 Capable Achievement*

44 Accepting my Portion in Life Tradition

49 Helpful Benevolence

51 Devout Tradition

*Item 43 was classified as falling under the primary motivation of Self-Direction under the common space solution of the weighted multidimensional scaling (WMDS) model.


Table 72 Raw Counts and Percentage Agreement of Value Statements Versus Primary Motivation Type for each Space

Primary

Motivation

Type

Space Schwartz's

Model Common

Space

(WMDS)


Power 5 5 4 3 5

100.0% 100.0% 80.0% 60.0%

Achievement 3 4 2 3 5

60.0% 80.0% 40.0% 60.0%

Hedonism 3 2 3 2 3

100.0% 66.7% 100.0% 66.7%

Stimulation 1 1 2 1 2

50.0% 50.0% 100.0% 50.0%

Self-Direction 6 4 5 4 7

85.7% 57.1% 71.4% 57.1%

Universalism 8 8 5 5 9

88.9% 88.9% 55.6% 55.6%

Benevolence 8 5 4 4 9

88.9% 55.6% 44.4% 44.4%

Tradition 3 3 3 4 5

60.0% 60.0% 60.0% 80.0%

Conformity 3 3 3 1 4

75.0% 75.0% 75.0% 25.0%

Security 6 6 6 4 7

85.7% 85.7% 85.7% 57.1%


Table 73 Correlations of Lin’s Concordance between the 56 Value Statements of the Schwartz

Social Values Scale Ratios via Kendall’s Tau-b by Country

Country

1 2 3

1. United States -

2. Poland .243* -

3. Romania .173* .381* -

* Correlation is significant at the 0.01 level (2-tailed).


Table 74 Correlations of Lin’s Concordance between the 56 Value Statements of the Schwartz

Social Values Scale Ratios via Pearson’s r by Country

Country

1. 2. 3.

1. United States -

2. Poland .399** - 3. Romanian .288** .579** -

** Correlation is significant at the 0.01 level (2-tailed).


Table 75 Correlations of Distances Derived through Classic Multidimensional Scaling between

the 56 Value Statements of the Schwartz Social Values Scale via Pearson’s r by

Country

Country

1 2 3

1. United States -

2. Poland .194* - 3. Romania .189* .218* -

*Correlation is significant at the 0.01 level (2-tailed)


Table 76

Correlations of Distances Derived through Classic Multidimensional Scaling via

Kendall’s Tau-b between the 56 Value Statements of the Schwartz Social Values Scale

by Country

Country

1 2 3

1. United States -

2. Poland .114* - 3. Romania .121* .144* -

*Correlation is significant at the 0.01 level (2-tailed)


Appendix A

The English version of the Schwartz Social Values Scale

Item

Number

Item

1 Equality (equal opportunity for all)

2 Inner Harmony (at peace with myself)

3 Social Power (control over others, dominance)

4 Pleasure (gratification of desires)

5 Freedom (freedom of action and thought)

6 A Spiritual Life (emphasis on spiritual not material matters)

7 Sense of Belonging (feeling that others care about me)

8 Social Order (stability of society)

9 An Exciting Life (stimulating experiences)

10 Meaning in Life (a purpose in life)

11 Politeness (courtesy, good manners)

12 Wealth (material possessions, money)

13 National Security (protection of my nation from enemies)

14 Self-Respect (belief in one’s own worth)

15 Reciprocation of Favors (avoidance of indebtedness)

16 Creativity (uniqueness, imagination)

17 A World at Peace (free of war and conflict)

18 Respect for Tradition (preservation of time-honored customs)

19 Mature Love (deep emotional and spiritual intimacy)

20 Self-Discipline (self-restraint, resistance to temptation)

21 Privacy (the right to have a private sphere)

22 Family Security (safety for loved ones)

23 Social Recognition (respect, approval by others)

24 Unity with Nature (fitting into nature)

25 A Varied Life (filled with challenge, novelty, and change)

26 Wisdom (a mature understanding of life)


Appendix A (Continued)


Item

Number

Item

27 Authority (the right to lead or command)

28 True Friendship (close, supportive friends)

29 A World of Beauty (beauty of nature and the arts)

30 Social Justice (correcting injustice, care for the weak)

31 Independent (self-reliant, self-sufficient)

32 Moderate (avoiding extremes of feeling and action)

33 Loyal (faithful to my friends, group)

34 Ambitious (hardworking, aspiring)

35 Broad-minded (tolerant of different ideas and beliefs)

36 Humble (modest, self-effacing)

37 Daring (seeking adventure, risk)

38 Protecting the Environment (preserving nature)

39 Influential (having an impact on people and events)

40 Honoring of Parent and Elders (showing respect)

41 Choosing Own Goals (selecting own purposes)

42 Healthy (not being sick physically or mentally)

43 Capable (competent, effective, efficient)

44 Accepting my Portion in Life (submitting to life’s circumstances)

45 Honest (genuine, sincere)

46 Preserving my Public Image (protecting my “face”)

47 Obedient (dutiful, meeting obligations)

48 Intelligent (logical, thinking)

49 Helpful (working for the welfare of others)

50 Enjoying Life (enjoying food, sex, leisure, etc.)

51 Devout (holding to religious faith and belief)

52 Responsible (dependable, reliable)


Appendix A (Continued)


Item

Number

Item

53 Curious (interested in everything, exploring)

54 Forgiving (willing to pardon others)

55 Successful (achieving goals)

56 Clean (neat, tidy)


Appendix B

The Full United States Survey SECTION I: INTERNET USAGE

1 About how long have you been using the Internet? O Less than 3 months O 4-12 months O 1-3 years O 4-6 years O 7 years or more 2 On average, how many hours per week, if any, do you use the Internet? O 0 O 1 - 5 O 6 - 10 O 11 – 15 O 16 - 20 O 21 - or more 3 About what percentage of your friends, relatives, and acquaintances would you

guess use the Internet at least once a week? O None O 1 – 25% O 26 – 50% O 51 – 75% O 76 – 100% 4 How often, if ever, do you go online to shop (look for information about products or

make a purchase? O Never O Less than once a month O 1-2 times a month O 3-5 times a month O 6-9 times a month O 10 or more times a month 5 As far as you know, how many years has online shopping been available to people in

the United States? O 1 year O 2 years O 3 years O 4 years O 5 years O 6 years O 7 years O 8 years O 9 years or more


6 What was the first year that people around you could find products of interest to

them for sale through the Internet? O Before 1990 O 1991 O 1992 O 1993 O 1994 O 1995 O 1996 O 1997 O 1998 O 1999 O 2000 O 2001 or later 7 About how long ago did your friends, family, or neighbors learn that they could shop

for products through the Internet? O 9 years ago or more O 8 years ago O 7 years ago O 6 years ago O 5 years ago O 4 years ago O 3 years ago O 2 years ago O 1 year ago O This current year 8 About what percentage of your friends, relatives, and acquaintances shop online? O None O 1 – 25% O 26 – 50% O 51 – 75% O 76 – 100% 9 Compared to other ways of shopping, how unusual or novel do you personally find

online shopping to be? Use a scale of 1-7, where 1 means not at all novel or unusual and 7 means very novel or unusual.


Not at all Novel or Unusual

Very Novel or Unusual

1 O 2 O 3 O 4 O 5 O 6 O 7 O 10 In general, how different is shopping online compared to shopping in traditional stores? Use a scale of 1-7, where 1 means not at all different and 7 means very different.

Not at all Different

Very Different

1 O 2 O 3 O 4 O 5 O 6 O 7 O 11 In general, how unique is shopping online compared to shopping at a traditional

store? Use a scale of 1-7, where 1 means not at all unique and 7 means very unique.

Not at all Unique

Very Unique

1 O 2 O 3 O 4 O 5 O 6 O 7 O 12 In general, how innovative is shopping online compared to shopping at a traditional

store? Use a scale of 1-7, where 1 means not at all innovative and 7 means very innovative.

Not at all

Innovative Very

Innovative 1 O 2 O 3 O 4 O 5 O 6 O 7 O

13 Please indicate how much you agree or disagree with the following statements about

your reactions to online shopping for those particular products/services of interest to you personally. Please indicate one answer for each statement, and react to all of the statements.

Strongly

Disagree Disagree Neither Agree nor Disagree Agree Strongly

Agree

a In general, I am among the last in my circle of friends to visit a shopping website when it appears. O O O O O

b If I heard that a new website was available for online shopping, I would be interested enough to visit it.

O O O O O

c Compared to my friends, I have visited few online shopping websites. O O O O O

d I will visit an online shopping website even if I know practically nothing about it. O O O O O

e I know the names of new online shopping sites before other people do. O O O O O

f In general, I am the last in my circle of friends to know about new websites. O O O O O


14 On average, how often do you shop (searching for product or service information, or making a purchase) on the Internet?

O Never [IF NEVER, CLICK THE BUTTON AND THEN CLICK HERE TO SKIP TO QUESTION #19] O Rarely O Less than once a month O About once a month O About once a week O Daily 15 Please indicate how much you agree or disagree with the following statements about

your reactions to online shopping for those particular products/services of interest to you personally. Please indicate one answer for each statement, and react to all of the statements.

Strongly Disagree Disagree Neither Agree

nor Disagree Agree Strongly Agree

a My opinion on online shopping seems not to count with other people. O O O O O

b When I consider online shopping, I ask other people for advice. O O O O O

c People that I know pick shopping sites based on what I have told them. O O O O O

d I don't need to talk to others before I do online shopping. O O O O O

e When they choose to do online shopping, other people do not turn to me for advice. O O O O O

f I rarely ask other people what online websites to shop. * O O O O O

g I often persuade people to try the online websites that I look at. O O O O O

h I like to get other's opinions before I shop at an online site. O O O O O

i I often influence people’s opinions about online shopping. O O O O O

j I feel more comfortable shopping at an online website after I have gotten other people’s opinions on it.

O O O O O

k Other people rarely come to me for advice about using an online shopping site. O O O O O

l When choosing an online shopping site, other people's opinions are not important to me. O O O O O


16 How often, if at all, do you VISIT each type of web site (WITHOUT purchasing) in order to help you to make a purchase decision? Use any number from 1 (never) to 5 (regularly). [INDICATE ONE RESPONSE FOR EACH ITEM]

Never Sometimes Regularly

a Clothing / Accessories. 1O 2O 3O 4 O 5 O b Books / Magazines. 1O 2O 3O 4 O 5 O c Travel. 1O 2O 3O 4 O 5 O d Health & Medical. 1O 2O 3O 4 O 5 O e Financial Services. 1O 2O 3O 4 O 5 O f Consumer electronics (TV, VCR, stereo, cellular

phone) 1O 2O 3O 4 O 5 O g Entertainment (compact disks, videos, concert

tickets) 1O 2O 3O 4 O 5 O h Computer hardware or software. 1O 2O 3O 4 O 5 O i Food / Beverage / Groceries. 1O 2O 3O 4 O 5 O j Home Appliances (refrigerator, dishwasher). 1O 2O 3O 4 O 5 O k Other. 1O 2O 3O 4 O 5 O

17 How often, if at all, do you PURCHASE any of the following items/services (and not

just look for information) online? Use any number from 1 (never) to 5 (regularly) . [INDICATE ONE RESPONSE FOR EACH ITEM]

Never Sometimes Regularly

a Clothing / Accessories. 1O 2O 3O 4 O 5 O b Books / Magazines. 1O 2O 3O 4 O 5 O c Travel. 1O 2O 3O 4 O 5 O d Health & Medical. 1O 2O 3O 4 O 5 O e Financial Services. 1O 2O 3O 4 O 5 O f Consumer electronics (TV, VCR, stereo, cellular

phone). 1O 2O 3O 4 O 5 O g Entertainment (compact disks, videos, concert

tickets). 1O 2O 3O 4 O 5 O h Computer hardware or software. 1O 2O 3O 4 O 5 O i Food / Beverage / Groceries. ) 1O 2O 3O 4 O 5 O j Home Appliances (refrigerator, dishwasher). 1O 2O 3O 4 O 5 O k Other. 1O 2O 3O 4 O 5 O

18 How much would the following encourage you to shop (visit or purchase) at a

particular website? Use any number from 1 (strongly discourages me) to 7 (strongly encourages me). [INDICATE ONE RESPONSE FOR EACH ITEM]

1 = Strongly Discourages Me

4 = Neither Encourages Nor Discourages Me

7 = Strongly Encourages Me

a The order process is easy to use. 1O 2O 3O 4O 5O 6O 7O b The products I am looking for are easy to find 1O 2O 3O 4O 5O 6O 7O


c The website is new and different 1O 2O 3O 4O 5O 6O 7O d Product price. 1O 2O 3O 4O 5O 6O 7O e Provides customer feedback (that is, the site

provides a place for you to learn about other customer’s evaluation of the product))

1O 2O 3O 4O 5O 6O 7O

f My friends and family have been happy when they have shopped there 1O 2O 3O 4O 5O 6O 7O

g Reputation and credibility of the company on the web. 1O 2O 3O 4O 5O 6O 7O

h It is enjoyable to visit. 1O 2O 3O 4O 5O 6O 7O i The delivery time is short. 1O 2O 3O 4O 5O 6O 7O j The site is in my primary language. 1O 2O 3O 4O 5O 6O 7O k My friends and family will like to know my opinions

of the site. 1O 2O 3O 4O 5O 6O 7O l A wide selection and variety of products. 1O 2O 3O 4O 5O 6O 7O m Low or no charge for shipping and handling. 1O 2O 3O 4O 5O 6O 7O n It has entertaining graphics and displays. 1O 2O 3O 4O 5O 6O 7O o Provides product information, including FAQs –

frequently asked questions. 1O 2O 3O 4O 5O 6O 7O p A good place to find a bargain. 1O 2O 3O 4O 5O 6O 7O q Providing credit card safety. 1O 2O 3O 4O 5O 6O 7O r Fast response time from customer service. 1O 2O 3O 4O 5O 6O 7O s I hear about it on the radio, television or in

newspapers . 1O 2O 3O 4O 5O 6O 7O t The download speed of the page. 1O 2O 3O 4O 5O 6O 7O u A return policy that is easy to understand and us. 1O 2O 3O 4O 5O 6O 7O v Price incentives (coupons, future sale items,

frequent shopper program, etc.). 1O 2O 3O 4O 5O 6O 7O w Interactive web design (try it on, design your

product / services). 1O 2O 3O 4O 5O 6O 7O

*YOU ARE HALF WAY THROUGH THE SURVEY, THANK YOU FOR YOUR PATIENCE.

SECTION II: OPINIONS AND BELIEFS 19 Please indicate whether the following statements are true or false of you.

True False a. I sometimes litter. T O F O b. Before voting I thoroughly investigate the qualifications of all the

candidates. T O F O c. I always admit my mistakes openly and face the potential

negative consequences. T O F O d. I never hesitate to go out of my way to help someone in trouble. T O F O e. In traffic I am always polite and considerate of others. T O F O f. It is hard for me to go on with my work if I am not encouraged. T O F O g. I always accept others’ opinions, even when they don’t agree

with my own. T O F O h. I have never intensely disliked anyone. T O F O i. I take out my bad moods on others now and then. T O F O


j. On occasion I have doubts about my ability to succeed in life. T O F O k. There has been an occasion when I took advantage of someone

else. T O F O l. I sometimes feel resentful when I don’t get my way. T O F O

m. In conversations I always listen attentively and let others finish their sentences. T O F O

n. I am always careful about my manner or dress. T O F O o. I never hesitate to help someone in case of emergency. T O F O p. My table manners at home are as good as when I eat out in a

restaurant. T O F O q. If I could get into a movie without paying and be sure that I was

not seen I would probably do it. T O F O r. When I have made a promise, I keep it – no ifs, ands, or buts. T O F O s. On a few occasions, I have given up doing something because I

thought too little of my ability. T O F O t. I occasionally speak badly of others behind their backs. T O F O u. I like to gossip at times. T O F O v. I would never live off/at other people’s expense. T O F O w. There have been times when I felt like rebelling against people in

authority even though I knew they were right. T O F O x. I always stay friendly and courteous with other people, even

when I am stressed out. T O F O y. No matter who I am talking to, I’m always a good listener. T O F O z. During arguments I always stay objective and matter-of-fact. T O F O

aa. I can remember “playing sick” to get out of something. T O F O bb. There has been at least one occasion when I failed to return an

item that I borrowed. T O F O cc. There have been occasions when I took advantage of someone. T O F O dd. I always eat a healthy diet. T O F O ee. I’m always willing to admit when I make a mistake. T O F O ff. Sometimes I only help because I expect something in return. T O F O

gg. I always try to practice what I preach. T O F O hh. I don’t find it particularly difficult to get along with loud-mouthed

obnoxious people. T O F O ii. I sometimes try to get even rather than to forgive and forget. T O F O jj. When I don’t know something I don’t at all mind admitting it. T O F O

kk. I am courteous even to people who are disagreeable. T O F O ll. At times I have really insisted on having things my own way. T O F O

mm. There have been occasions when I felt like smashing things. T O F O nn. I would never think of letting someone else be punished for my

wrongdoings. T O F O oo. I never resent being asked to return a favor. T O F O pp. I have never been irked when people expressed ideas very

different from my own. T O F O qq. I never make a long trip without checking the safety of my car. T O F O rr. There have been times when I have been quite jealous of the

good fortune of others. T O F O ss. I have almost never felt the urge to tell someone off. T O F O tt. I am sometimes irritated by people who ask favors of me. T O F O

uu. I have never felt that I was punished without cause. T O F O


vv. I sometimes think that when people have a misfortune they only got what they deserved. T O F O

ww. I have never deliberately said something that hurt someone’s feelings. T O F O

SECTION III: VALUES

20 The following items deal with what values YOU THINK are important. Please rate each value as a guiding principle IN YOUR LIFE, using a scale from –1 to 7, where –1 means “opposed to my values” and 7 means “of supreme importance”. Please indicate one number for each value concept.

-1 = opposed to my values

7 = of supreme importance

a Equality (equal opportunity for all) -1 O 0O 1 O 2O 3O 4 O 5O 6 O 7O

b Inner Harmony (at peace with myself) -1 O 0O 1 O 2O 3O 4 O 5O 6 O 7O

c Social Power (control over others, dominance) -1 O 0O 1 O 2O 3O 4 O 5O 6 O 7O

d Pleasure (gratification of desires) -1 O 0O 1 O 2O 3O 4 O 5O 6 O 7O

e Freedom (freedom of action and thought) -1O 0O 1 O 2O 3O 4 O 5O 6O 7O

f A Spiritual Life (emphasis on spiritual not material -1O 0O 1 O 2O 3O 4 O 5O 6O 7O


matters)

g Sense of Belonging (feeling that others care about me)

-1O 0O 1 O 2O 3O 4 O 5O 6O 7O

h Social Order (stability of society) -1O 0O 1 O 2O 3O 4 O 5O 6O 7O

i An Exciting Life (stimulating experiences) -1O 0O 1 O 2O 3O 4 O 5O 6O 7O

j Meaning in Life (a purpose in life) -1O 0O 1O 2O 3O 4O 5O 6O 7O

k Politeness (courtesy, good manners) -1O 0O 1O 2O 3O 4O 5O 6O 7O

l Wealth (material possessions, money) -1O 0O 1O 2O 3O 4O 5O 6O 7O

m National Security (protection of my nation from enemies)

-1O 0O 1O 2O 3O 4O 5O 6O 7O

n Self-Respect (belief in one’s own worth) -1O 0O 1O 2O 3O 4O 5O 6O 7O

o Reciprocation of Favors (avoidance of indebtedness)

-1O 0O 1O 2O 3O 4O 5O 6O 7O

p Creativity (uniqueness, imagination) -1O 0O 1O 2O 3O 4O 5O 6O 7O

q A World at Peace (free of war and conflict) -1O 0O 1O 2O 3O 4O 5O 6O 7O

r Respect for Tradition (preservation of time-honored customs)

-1O 0O 1O 2O 3O 4O 5O 6O 7O

s Mature Love (deep emotional and spiritual intimacy)

-1O 0O 1O 2O 3O 4O 5O 6O 7O

t Self-Discipline (self-restraint, resistance to temptation)

-1O 0O 1O 2O 3O 4O 5O 6O 7O

u Privacy (the right to have a private sphere) -1O 0O 1O 2O 3O 4O 5O 6O 7O



v Family Security (safety for loved ones) -1O 0O 1O 2O 3O 4O 5O 6O 7O

w Social Recognition (respect, approval by others)

-1O 0O 1O 2O 3O 4O 5O 6O 7O

x Unity with Nature (fitting into nature) -1O 0O 1O 2O 3O 4O 5O 6O 7O

y A Varied Life (filled with challenge, novelty, and -1O 0O 1O 2O 3O 4O 5O 6O 7O


change)

z Wisdom (a mature understanding of life -1O 0O 1O 2O 3O 4O 5O 6O 7O

aa Authority (the right to lead or command) -1O 0O 1O 2O 3O 4O 5O 6O 7O

bb True Friendship (close, supportive friends) -1O 0O 1O 2O 3O 4O 5O 6O 7O

cc A World of Beauty (beauty of nature and the arts)

-1O 0O 1O 2O 3O 4O 5O 6O 7O

dd Social Justice (correcting injustice, care for the weak)

-1O 0O 1O 2O 3O 4O 5O 6O 7O

ee Independent (self-reliant, self-sufficient) -1O 0O 1O 2O 3O 4O 5O 6O 7O

ff Moderate (avoiding extremes of feeling and action)

-1O 0O 1O 2O 3O 4O 5O 6O 7O

gg Loyal (faithful to my friends, group) -1O 0O 1O 2O 3O 4O 5O 6O 7O

hh Ambitious (hardworking, aspiring) -1O 0O 1O 2O 3O 4O 5O 6O 7O

ii Broad-minded (tolerant of different ideas and beliefs)

-1O 0O 1O 2O 3O 4O 5O 6O 7O

jj Humble (modest, self-effacing) -1O 0O 1O 2O 3O 4O 5O 6O 7O

kk Daring (seeking adventure, risk) -1O 0O 1O 2O 3O 4O 5O 6O 7O

ll Protecting the Environment (preserving nature)

-1O 0O 1O 2O 3O 4O 5O 6O 7O

mm Influential (having an impact on people and events)

-1O 0O 1O 2O 3O 4O 5O 6O 7O

nn Honoring of Parent and Elders (showing respect) -1O 0O 1O 2O 3O 4O 5O 6O 7O

oo Choosing Own Goals (selecting own purpose) -1O 0O 1O 2O 3O 4O 5O 6O 7O

pp Healthy (not being sick physically or mentally) -1O 0O 1O 2O 3O 4O 5O 6O 7O

qq Capable (competent, effective, efficient) -1O 0O 1O 2O 3O 4O 5O 6O 7O

rr Accepting my Portion in Life (submitting to life’s circumstances)

-1O 0O 1O 2O 3O 4O 5O 6O 7O

ss Honest (genuine, sincere) -1O 0O 1O 2O 3O 4O 5O 6O 7O

tt Preserving my Public Image (protecting my “face”)

-1O 0 O 1O 2O 3O 4O 5O 6O 7O

uu Obedient (dutiful, meeting obligations) -1O 0 O 1O 2O 3O 4O 5O 6O 7O




vv Intelligent (logical, thinking) -1O 0 O 1O 2O 3O 4O 5O 6O 7O

ww Helpful (working for the welfare of others) -1O 0 O 1O 2O 3O 4O 5O 6O 7O

xx Enjoying Life (enjoying food, sex, leisure, etc.) -1O 0 O 1O 2O 3O 4O 5O 6O 7O

yy Devout (holding to religious faith and belief) -1O 0O 1O 2O 3O 4O 5O 6O 7O

zz . Responsible (dependable, reliable) -1O 0O 1O 2O 3O 4O 5O 6O 7O

aaa Curious (interested in everything, exploring) -1O 0O 1O 2O 3O 4O 5O 6O 7O

bbb Forgiving (willing to pardon others) -1O 0O 1O 2O 3O 4O 5O 6O 7O

ccc Successful (achieving goals) -1O 0O 1O 2O 3O 4O 5O 6O 7O

ddd Clean (neat, tidy) -1O 0O 1O 2O 3O 4O 5O 6O 7O eee Self-Indulgent (doing

pleasant things) -1O 0O 1O 2O 3O 4O 5O 6O 7O *JUST A FEW MORE QUESTIONS, YOU ARE ALMOST FINISHED.

SECTION IV: BACKGROUND INFORMATION 21 What is your gender? O Male O Female 22 How old are you (in years)? _________________ 23 What is your marital status? O Single, never been married O Married O Separated/Divorced O Widowed 24 In what state is your permanent address at this current time?

25 Were your grandparents born in the U.S.A.? O Yes, all four of them O Yes, 1, 2, or 3 of them


O None of them O Don’t know 26 Were your parents born in the U.S.A.? O Neither O My mother O My father O Both O Don’t know 27 Were you born in the U.S.A.? O Yes O No O Don’t know 28 Would you describe your family as mainly: O African American O Taiwanese American O Chinese (mainland) American O Iranian American O Palestinian American O German/Austrian American O Hispanic American O Polish American O Greek American O Romanian American O Canadian O Other (please specify) ____________________ 29 What was the last year of education you completed? O Some high school O High school O Technical School/Training (such as auto mechanic) O Some college/university O College/university graduate O Graduate or professional school 30 What is your current employment? [CHECK ALL THAT APPLY] Employed-full time [GO TO Q36] Employed-part time [GO TO Q36] Self employed [GO TO Q36] (self) Temporarily unemployed [GO TO Q37]


Student [GO TO Q37] Homemaker/housewife [GO TO Q37] Retired [GO TO Q37] 31 (IF EMPLOYED) What is your occupation? O Professional O Managerial/Executive O Sales O Clerical O Labor with technical training O Labor without technical training O Other (please specify) _____________________ 32 Please indicate which of the following categories best represents your annual

household income before taxes. (income) O $10,000 or less O $10,001 to $20,000 O $20,001 to $30,000 O $30,001 to $40,000 O $40,001 to $50,000 O $50,001 to $75,000 O $75,001 to $100,000 O more than $100,000 33 How many people live in your household, including yourself (please enter the number)? __________________ 34 Please indicate whether you own each of the following items. [INDICATE ONE RESPONSE FOR EACH] Yes No Don’t Know

a A personal computer O O O b A DVD player O O O c A high-definition TV (HDTV) O O O d A Personal Digital Assistant

(PDA) O O O


Appendix C

The Full Polish Survey in its Original English INTERNET USAGE

1. Is there a personal computer in your home? [CHECK ONE] 1 Yes 2 No 2. About what percentage of your friends, relatives, and acquaintances would you guess use the Internet at least once a week? [CHECK ONE] 1 None 2 1 - 25% 3 26 - 50% 4 51 - 75% 5 76 - 100% 3. About how many, if any, of your friends, relatives, and acquaintances shop online? [CHECK ONE] 1 None 2 1 - 25% 3 26 - 50% 4 51 - 75% 5 76 - 100% 4. About how long ago did your friends, family, or neighbors, learn that they could purchase products on the Internet? [CHECK YOUR ONE BEST GUESS] 1 Over 10 years ago 2 10 years ago 3 9 years ago 4 8 years ago 5 7 years ago 6 6 years ago 7 5 years ago 8 4 years ago 9 3 years ago 10 2 years ago 11 1 year ago 12 This current year 5. What was the first year that people living in your hometown could find products of interest to them for sale on the Internet? [CHECK ONE]

Before

1990 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2000

or later 6. AS FAR AS YOU KNOW, for about how many years has online shopping been available to people in Poland? [CHECK ONE]

1 year 2 years 3 years 4 years 5 years 6 years 7 years 8 years 9 years 9 years


or more 7. Have you ever used the World-Wide Web (WWW) before? [CHECK ONE] 1 Yes [GO TO Q8] 2 No [GO TO Q27] 8. Where do you use the Internet most often? [CHECK ONE] 1 Home 2 School 3 Work 4 Other 9. On average, how many hours per week, if any, do you use the Internet? [CHECK ONE] 1 0 2 1 - 5 3 6 - 10 4 11 - 15 5 16 - 20 6 21 - or more 10. About how long have you been using the Internet? [CHECK ONE] 1 Less than 3 months 2 4 - 12 months 3 1 - 3 years 4 4 - 6 years 5 7 - 9 years 6 10 years or more 11. On average, how often do you do each of the following on the Internet? [CHECK ONE RESPONSE FOR EACH ITEM] Rarely or

Never Less Than

Once a Month

About Once A Month

About Once A Week

Daily

a. Shopping (that is, searching for product or service information, or making a purchase)

1 2 3 4 5

b. Entertainment (game playing, etc.) 1 2 3 4 5 c. Communication with others (E-mail, Voice-mail, BBS,

ICQ, Instant Messenger, etc.) 1 2 3 4 5

d. Education and newspaper reading (e-books, e-magazines, etc.)

1 2 3 4 5


DISTANCE SHOPPING INFORMATION

Distance shopping could be defined as any form of shopping that is not practiced through the traditional store. Distance shopping could be considered shopping through magazines, catalogs, telemarketing, online etc. 12. How often, if ever do you do any distance shopping through methods other than the Internet? For the following items, 1 means rarely or never and 5 means daily. [CHECK ONE RESPONSE FOR EACH ITEM] Rarely or

Never Less Than

Once a Month

About Once a Month

About Once a Week

Daily

a. Through catalogs, brochures, or magazines. 1 2 3 4 5 b. Through television or infomercials. 1 2 3 4 5 c. Through phone orders exclusively (examples: supermarkets or pizza delivery).

1 2 3 4 5

SHOPPING ONLINE

“Shopping online,” means using the Internet to look for information about products, services, manufacturers, or companies and/or making purchases of products, services, etc.

13. Given what you get from Internet and traditional stores, what do you think the prices of the Internet stores ought to be? [CHECK ONE]

1 The price ought to be a lot higher than those of the traditional store. 2 The price ought to be a little bit higher than those of the traditional store. 3 The prices ought to be the same with those of the traditional store. 4 The prices ought to be a bit lower than those of the traditional store. 5 The prices ought be a lot lower than those of the traditional store


14. How much would the following encourage you to shop (visit or purchase) at a particular website? For the following items, 1 means strongly discourages me and 7 means strongly encourages me. [CHECK ONE RESPONSE FOR EACH ITEM] 1 = Strongly

Discourages Me 4 = Neither

Encourages Nor Discourages Me

7 = Strongly Encourages Me

a. The order process is easy to use 1 2 3 4 5 6 7 b. Easy to find the product I am looking for 1 2 3 4 5 6 7 c. The website is new and different 1 2 3 4 5 6 7 d. Product price 1 2 3 4 5 6 7 e. Provides customer feedback (that is, the site provides a place for you to learn about other customer’s evaluations of the product).

1 2 3 4 5 6 7

f. My friends and family have been happy when they have shopped there

1 2 3 4 5 6 7

g. Reputation and credibility of the company on the web

1 2 3 4 5 6 7

h. It is enjoyable to visit 1 2 3 4 5 6 7 i. The delivery time is short 1 2 3 4 5 6 7 j. The site is enjoyable to visit 1 2 3 4 5 6 7 k. My friends and family will like to know my opinions of the site

1 2 3 4 5 6 7

l. A wide selection and variety of products 1 2 3 4 5 6 7 m. Low or no shipping charges 1 2 3 4 5 6 7 n. It has entertaining graphics and displays 1 2 3 4 5 6 7 o. Provides product information, including FAQ – frequently asked questions

1 2 3 4 5 6 7

p. A good place to find a bargain 1 2 3 4 5 6 7 q. Provides credit card safety 1 2 3 4 5 6 7 r. Fast response time from customer service 1 2 3 4 5 6 7 s. I hear about it on the radio, television, or in newspapers

1 2 3 4 5 6 7

t. The download speed of the pages 1 2 3 4 5 6 7 u. The return policy is easy to use 1 2 3 4 5 6 7 v. Offers good price incentives (coupons, featured sale items, frequent shopper program, etc.)

1 2 3 4 5 6 7

w. Interactive website design (try it on, design your product/services)

1 2 3 4 5 6 7

15. How often, if ever, do you go online to shop (look for information or make a purchase)? [CHECK ONE] 1 Never [GO TO Q25] 2 Less than once a month [GO TO Q16] 3 1-2 times/month [GO TO Q16]


4 3-5times/month [GO TO Q16] 5 6-9 times/month [GO TO Q16] 6 10 or more times/month [GO TO Q16] 16. How often, if at all, do you VISIT each type of web site (WITHOUT purchasing) in order to help you to make a purchase decision? [CHECK ONE RESPONSE FOR EACH ITEM] Never Seldom Sometimes Often Very Often

a. Clothing / Accessories. 1 2 3 4 5 b. Books / Magazines. 1 2 3 4 5 c. Travel. 1 2 3 4 5 d. Health & Medical. 1 2 3 4 5 e. Financial Services. 1 2 3 4 5 f. Consumer electronics (TV, VCR, stereo,

cellular phone) 1 2 3 4 5

g. Entertainment (compact disks, videos, concert tickets).

1 2 3 4 5

h. Computer hardware or software. 1 2 3 4 5 i. Food / Beverage / Groceries. 1 2 3 4 5 j. Home Appliances (refrigerator,

dishwasher). 1 2 3 4 5

k. Other. 1 2 3 4 5 17. All in all, how similar is shopping online to going to a traditional store? Using a scale of 1-7, where 1 means very different and 7 means very similar. [CHECK ONE]

Very Different

Very Similar

1 2 3 4 5 6 7 18. Compared to other ways of shopping, how unusual or novel do you personally find online shopping to be? Using a scale of 1-7, where 1 means not at all novel or unusual and 7 means very novel or unusual. [CHECK ONE]

Not at All Novel or Unusual

Very Novel or Unusual

1 2 3 4 5 6 7 19. All in all, how unique is shopping online to going to a traditional store? Using a scale of 1-7, where 1 means not at all unique and 7 means very unique. [CHECK ONE]

Not at all Unique

Very Unique


1 2 3 4 5 6 7 20. Compared to what you find in traditional stores, how often are you surprised by the ads, products, prices, displays, and other features of online shopping? [CHECK ONE]

1 I am surprised much more often online. 2 I am surprised somewhat more often online 3 I am surprised slightly more often online 4 I am surprised equally often online and in traditional stores 5 I am surprised slightly more often in traditional stores 6 I am surprised somewhat more often in traditional stores 7 I am surprised much more often in traditional stores 8 Don’t Know

21. All in all, how innovative is shopping online to going to a traditional store? Using a scale of 1-7, where 1 means not at all innovative and 7 means highly innovative. [CHECK ONE]

Not at all Innovative

Highly Innovative

1 2 3 4 5 6 7 22. Do you find shopping through the Internet pleasant or unpleasant? Using a scale of 1-7, where 1 means unpleasant and 7 means pleasant. [CHECK ONE] Unpleasant

Pleasant

1 2 3 4 5 6 7 23. Overall, how satisfied or dissatisfied are you with shopping in the Internet? Using a scale of 1-7, where 1 means not very at all satisfied and 7 means very satisfied. [CHECK ONE]

Very Dissatisfied

Neither Satisfied nor Dissatisfied

Very Satisfied

1 2 3 4 5 6 7 24. How often, if ever, do you PURCHASE (and not just look for information) online? [CHECK ONE RESPONSE FOR EACH ITEM] Never Seldom Sometimes Often Very Oftena. Clothing / Accessories. 1 2 3 4 5 b. Books / Magazines. 1 2 3 4 5 c. Travel. 1 2 3 4 5


d. Health & Medical. 1 2 3 4 5 e. Financial Services. 1 2 3 4 5 f. Consumer electronics (TV, VCR, stereo, cellular phone)

1 2 3 4 5

g. Entertainment (compact disks, videos, concert tickets).

1 2 3 4 5

h. Computer hardware or software. 1 2 3 4 5 i. Food / Beverage / Groceries. 1 2 3 4 5 j. Home Appliances (refrigerator, dishwasher).

1 2 3 4 5

k. Other. 1 2 3 4 5 25. How much do you agree or disagree with the following statements about your reactions to online shopping for those particular products/services of interest to you personally? [CHECK ONE RESPONSE FOR EACH ITEM] Strongly

Disagree Disagree Neither

Agree Nor

Disagree

Agree Strongly Agree

a. My opinion on online shopping seems not to count with other people.

1 2 3 4 5

b. When I consider online shopping, I ask other people for advice.

1 2 3 4 5

c. In general, I am among the last in my circle of friends to visit a shopping website when it appears.

1 2 3 4 5

d. People that I know pick shopping sites based on what I have told them.

1 2 3 4 5

e. I don’t need to talk to others before I do online shopping. 1 2 3 4 5 f. If I heard that a new website was available for online shopping, I would be interested enough to visit it.

1 2 3 4 5

g. When they choose to do online shopping, other people do not turn to me for advice.

1 2 3 4 5

h. I rarely ask other people what online websites to shop. 1 2 3 4 5 i. Compared to my friends, I have visited few online shopping websites.

1 2 3 4 5

j. I often persuade people to try the online shopping websites that I look at.

1 2 3 4 5

k. I like to get other’s opinions before I shop at an online site.

1 2 3 4 5

l. I will visit an online shopping website even if I know practically nothing about it.

1 2 3 4 5

m. I often influence people’s opinions about online shopping.

1 2 3 4 5

n. I feel more comfortable shopping at an online website after I have gotten other people’s opinions on it.

1 2 3 4 5

o. I know the names of new online shopping sites before other people do.

1 2 3 4 5

p. Other people rarely come to me for advice about using an online shopping site.

1 2 3 4 5

q. When choosing an online shopping site, other people’s opinions are not important to me.

1 2 3 4 5


r. Usually, my friends know the name of a new shopping website before I do.

1 2 3 4 5

26. How much do you agree or disagree about the following statements with regard to yourself? [CHECK ONE RESPONSE FOR EACH ITEM] Strongly

Disagree Disagree Neither

Agree nor

Disagree

Agree Strongly Agree

a. I am suspicious of new inventions and ways of thinking

1 2 3 4 5

b. I am reluctant about adopting new ways of doing things until I see them working for people around me

1 2 3 4 5

c. I rarely trust new ideas until I can see whether the vast majority of people around me accept them

1 2 3 4 5

d. I am generally cautious about accepting new ideas 1 2 3 4 5 e. I must see other people using new innovations before I will consider them

1 2 3 4 5

f. I often find my self skeptical of new ideas 1 2 3 4 5 g. I am aware that I am usually one of the last people in my group to accept something new

1 2 3 4 5

h. I tend to feel that the traditional way of living and doing things is the best way

1 2 3 4 5

i. I consider myself to be creative and original in my thinking and behavior

1 2 3 4 5

j. I am an inventive kind of person 1 2 3 4 5 k. I seek out new ways to do things 1 2 3 4 5 l. I enjoy trying out new things 1 2 3 4 5 m. I am challenged by ambiguities and unsolved problems

1 2 3 4 5

n. I find it stimulating to be original in my thinking and behavior.

1 2 3 4 5

o. I am receptive to new ideas 1 2 3 4 5 p. I frequently improvise methods for solving a problem when an answer is not apparent

1 2 3 4 5

q. I fell that I am an influential member of my peer group

1 2 3 4 5

r. My peers often ask me for advice or information 1 2 3 4 5

ATTITUDES AND OPINIONS


s. I enjoy taking part in the leadership responsibilities of the groups I belong to

1 2 3 4 5

t. I am challenged by unanswered questions 1 2 3 4 5 27. For the following statements check True for those that are truth about yourself and False for those that do not represent yourself. [CHECK ONE RESPONSE FOR EACH ITEM]

True False

a. I sometimes litter. 1 2 b. I always admit my mistakes openly and face the potential negative consequences. 1 2 c. In traffic I am always polite and considerate of others. 1 2 d. I always accept others’ opinions, even when they don’t agree with my own. 1 2 e. I take out my bad moods on others now and then. 1 2 f. There has been an occasion when I took advantage of someone else. 1 2 g. In conversations I always listen attentively and let others finish their sentences. 1 2 h. I never hesitate to help someone in case of emergency. 1 2 i. When I have made a promise, I keep it – no ifs, ands, or buts. 1 2 j. I occasionally speak badly of others behind their backs. 1 2 k. I would never live off/at other people’s expense. 1 2 l. I always stay friendly and courteous with other people, even when I am stressed out. 1 2 m. During arguments I always stay objective and matter-of-fact. 1 2 n. There has been at least one occasion when I failed to return an item that I borrowed 1 2 o. I always eat a health diet. 1 2 p. Sometimes I only help because I expect something in return 1 2 q. Before voting I thoroughly investigate the qualifications of all the candidates. 1 2 r. I never hesitate to go out of my way to help someone in trouble. 1 2 s. It is sometimes hard for me to go on with my work if I am not encouraged. 1 2 t. I have never intensely disliked anyone. 1 2 u. On occasions I have had doubts about my ability to succeed in life. 1 2 v. I sometimes feel resentful when I don’t get my way. 1 2 w. I am always careful about my manner of dress. 1 2 x. My table manners at home are as good as when I eat out in a restaurant. 1 2 y. If I could get into a movie without paying and be sure I was not seen I would probably do it.

1 2

z. On a few occasions, I have give up something because I thought too little of my ability. 1 2 aa. I like to gossip at times. 1 2 bb. There have been times when I felt like rebelling against people in authority even though I knew they were right.

1 2

cc. No matter who I’m talking to, I’m always a good listener. 1 2 dd. I can remember “playing sick” to get out of something. 1 2 ee. There have been occasions when I have taken advantage of someone. 1 2 ff. I’m always willing to admit it when I make a mistake. 1 2 gg. I always try to practice what I preach. 1 2 hh. I don’t find it particularly difficult to get along with loudmouthed, obnoxious people. 1 2 ii. I sometimes try to get even rather than forgive and forget. 1 2 jj. When I don’t know something I don’t mind at all admitting it. 1 2


kk. I am always courteous, even to people who are disagreeable. 1 2 ll. At times I have really insisted on having things my own way. 1 2 mm. There have been occasions when I felt like smashing things. 1 2 nn. I would never think of letting someone else be punished for my wrong-doings. 1 2 oo. I never resent being asked to return a favor. 1 2 pp. I have never been irked when people expressed ideas very different from my own. 1 2 qq. I never make a long trip without checking the safety of my car. 1 2 rr. There have been times when I was quite jealous of the good fortune of others. 1 2 ss. I have almost never felt the urge to tell someone off. 1 2 tt. I am sometimes irritated by people who ask favors of me. 1 2 uu. I have never felt that I was punished without cause. 1 2 vv. I sometimes think when people have a misfortune they only got what they deserved. 1 2 ww. I have never deliberately said something that hurt someone’s feelings. 1 2

28. This section is composed of 57 items concerning what values YOU THINK are

important. Please rate each value as a guiding principle IN YOUR LIFE, using a scale

from 7 (of supreme importance) to 0 (not important) and -1 (opposed to my values).

Please indicate one number for each value concept:

a. Equality (equal opportunity for all)

-1 0 1 2 3 4 5 6 7

b. Inner Harmony (at peace with myself)

-1 0 1 2 3 4 5 6 7

c. Social Power (control over others, dominance)

-1 0 1 2 3 4 5 6 7

d. Pleasure (gratification of desires)

-1 0 1 2 3 4 5 6 7

e. Freedom (freedom of action and thought)

-1 0 1 2 3 4 5 6 7

f. A Spiritual Life (emphasis on spiritual not material matters)

-1 0 1 2 3 4 5 6 7

g. Sense of Belonging (feeling that others care about me)

-1 0 1 2 3 4 5 6 7

h. Social Order (stability of society)

-1 0 1 2 3 4 5 6 7

i. An Exciting Life (stimulating experiences)

-1 0 1 2 3 4 5 6 7

j. Meaning in Life (a purpose in life)

-1 0 1 2 3 4 5 6 7

k. Politeness (courtesy, good manners)

-1 0 1 2 3 4 5 6 7

l. Wealth (material possessions, money)

-1 0 1 2 3 4 5 6 7

m. National Security (protection of my nation from enemies)

-1 0 1 2 3 4 5 6 7

n. Self-Respect (belief in -1 0 1 2 3 4 5 6 7

-1 = Opposed to My Values

0 = Not Important

7 = Supremely Important


one’s own worth) o. Reciprocation of Favors (avoidance of indebtedness)

-1 0 1 2 3 4 5 6 7

p. Creativity (uniqueness, imagination)

-1 0 1 2 3 4 5 6 7

q. A World at Peace (free of war and conflict)

-1 0 1 2 3 4 5 6 7

r. Respect for Tradition (preservation of time-honored customs)

-1 0 1 2 3 4 5 6 7

s. Mature Love (deep emotional and spiritual intimacy)

-1 0 1 2 3 4 5 6 7

t. Self-Discipline (self-

restraint, resistance to

temptation)

-1 0 1 2 3 4 5 6 7

u. Privacy (the right to have a private sphere)

-1 0 1 2 3 4 5 6 7

v. Family Security (safety for loved ones)

-1 0 1 2 3 4 5 6 7

w. Social Recognition (respect, approval by others)

-1 0 1 2 3 4 5 6 7

x. Unity with Nature (fitting into nature)

-1 0 1 2 3 4 5 6 7

y. A Varied Life (filled with challenge, novelty, and change)

-1 0 1 2 3 4 5 6 7

z. Wisdom (a mature understanding of life)

-1 0 1 2 3 4 5 6 7

aa. Authority (the right to lead or command)

-1 0 1 2 3 4 5 6 7

bb. True Friendship (close, supportive friends)

-1 0 1 2 3 4 5 6 7

cc. A World of Beauty (beauty of nature and the arts)

-1 0 1 2 3 4 5 6 7

dd. Social Justice (correcting injustice, care for the weak)

-1 0 1 2 3 4 5 6 7

ee. Independent (self-reliant, self-sufficient)

-1 0 1 2 3 4 5 6 7

ff. Moderate (avoiding extremes of feeling and action)

-1 0 1 2 3 4 5 6 7


0 = Not Important



gg. Loyal (faithful to my friends, group)

-1 0 1 2 3 4 5 6 7

hh. Ambitious (hardworking, aspiring)

-1 0 1 2 3 4 5 6 7

ii. Broad-minded (tolerant of different ideas and beliefs)

-1 0 1 2 3 4 5 6 7

jj. Humble (modest, self-effacing)

-1 0 1 2 3 4 5 6 7

kk. Daring (seeking adventure, risk)

-1 0 1 2 3 4 5 6 7

ll. Protecting the Environment (preserving nature)

-1 0 1 2 3 4 5 6 7

mm. Influential (having an impact on people and events)

-1 0 1 2 3 4 5 6 7

nn. Honoring of Parent and Elders (showing respect)

-1 0 1 2 3 4 5 6 7

oo. Choosing Own Goals (selecting own purposes)

-1 0 1 2 3 4 5 6 7

pp. Healthy (not being sick physically or mentally)

-1 0 1 2 3 4 5 6 7

qq. Capable (competent, effective, efficient)

-1 0 1 2 3 4 5 6 7

rr. Accepting my Portion in Life (submitting to life’s circumstances)

-1 0 1 2 3 4 5 6 7

ss. Honest (genuine, sincere) -1 0 1 2 3 4 5 6 7 tt. Preserving my Public Image (protecting my “face”)

-1 0 1 2 3 4 5 6 7

uu. Obedient (dutiful, meeting obligations)

-1 0 1 2 3 4 5 6 7

vv. Intelligent (logical, thinking)

-1 0 1 2 3 4 5 6 7

ww. Helpful (working for the welfare of others)

-1 0 1 2 3 4 5 6 7

xx. Enjoying Life (enjoying food, sex, leisure, etc.)

-1 0 1 2 3 4 5 6 7

yy. Devout (holding to religious faith and belief)

-1 0 1 2 3 4 5 6 7

zz. Responsible (dependable, reliable)

-1 0 1 2 3 4 5 6 7

aaa. Curious (interested in everything, exploring)

-1 0 1 2 3 4 5 6 7

bbb. Forgiving (willing to pardon others)

-1 0 1 2 3 4 5 6 7

ccc. Successful (achieving goals)

-1 0 1 2 3 4 5 6 7

ddd. Clean (neat, tidy) -1 0 1 2 3 4 5 6 7 eee. Self-Indulgent (doing pleasant things)

-1 0 1 2 3 4 5 6 7

29. Is there a DVD player in your home? [CHECK ONE]


0 = Not Important


BACKGROUND INFORMATION


1 Yes 2 No 30. Is there a Digital TV in your home? [CHECK ONE] 1 Yes 2 No 31. Do you own a PDA (Personal Digital Assistant)? [CHECK ONE] 1 Yes 2 No 32. Do you have any major credit cards in your name (e.g., American Express, Diners Club, MasterCard)? [CHECK ONE] 1 Yes 2 No 33. Were you born within the Poland? [CHECK ONE] 1 Yes 2 No 34. Were your parents born in Poland [CHECK ONE] 1 Neither 2 My mother 3 My father 4 Both 35. Were your grandparents born in Poland? [CHECK ONE]

1 Yes, all four of them 2 Yes, 1, 2, or 3 of them 3 None of them 4 Don’t know

36. Where is your permanent address at this time? [CHECK ONE]

1 Warsaw 2 Within the Mazowieckie Vovoidship 3 Outside of the Mazowieckie Vovoidship

37. How many people live in your household, including yourself? ______ 38. How many of the people in your household are 17 years old or younger? ______ 39. How old are you? ______ 40. What is your gender? 1 Male 2 Female 41. What is your marital status? [CHECK ONE] 1 Single, never been married 2 Married


3 Separated, Divorced, or Widowed 42. What was the last year of education you completed? [CHECK ONE] 1 Some high school 2 High school 3 Technical School/Training (such as auto mechanic) 4 Some College/University 5 College/University Graduate 6 Graduate or professional school 43. What is your religious affiliation? [CHECK ONE] 1 Roman Catholic 2 Protestant 3 Russian Orthodox 4 Other Christian 5 Agnostic 6 Atheist 7 None 8 Other (please specify) __________________ 44. What is your current employment? [CHECK ALL THAT APPLY] 1 Employed – Full Time [GO TO Q45] 2 Employed – Part Time [GO TO Q45] 3 Temporarily Unemployed [GO TO Q45] 4 Self-Employed [GO TO Q45] 5 Student [GO TO Q46] 6 Homemaker/Housewife [GO TO Q46] 7 Retired [GO TO Q46] 45. [IF EMPLOYED] What is your occupation? [CHECK ONE] 1 Professional [CONTINUE TO Q46] 2 Managerial/Executive [CONTINUE TO Q46] 3 Sales [CONTINUE TO Q46] 4 Clerical [CONTINUE TO Q46] 5 Labor with Technical Training [CONTINUE TO Q46] 6 Labor without Technical Training [CONTINUE TO Q46] 46. Please indicate which of the following categories best represents your monthly household income before taxes? [CHECK ONE] 1 0 - 500 zlotny 2 500 - 1000 zl 3 1001 - 1500 zl

4 1501 - 1750 zl 5 1751 - 2000 zl 6 2001 - 2250 zl 7 2251 - 2500 zl 8 2501 - 2750 zl 9 2751 - 3000 zl 10 3001 - 3250 zl 11 3251 - 3500 zl 12 3501 - 4000 zl


13 4001 - 4500 zl 14 4501 or more per month

generalization schwartz social values scale 1...

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